forecasting-btc-arima-xgboost-prophet-lstm.ipynb

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    "# **Forecasting Bitcoin Prices via ARIMA, XGBOOST, PROPHET, and LSTM**\n",
    "\n",
    "- Accompanying Medium Blog: https://floreani.medium.com/how-well-can-machine-learning-models-predict-the-price-of-bitcoin-f036fdecdc03\n",
    "- Kaggle Dataset: https://www.kaggle.com/mczielinski/bitcoin-historical-data\n",
    "> \n",
    "<img src=\"https://media.giphy.com/media/8yQady2pFVfGJSnde7/giphy-downsized.gif\">\n",
    "\n",
    "\n",
    "### **Introduction**\n",
    "> \n",
    "The cryptocurrency Bitcoin continues to make world headlines and rise in popularity as more and more people/organizations begin adopting it. In this notebook, I will test the Bitcoin forecasting abilities of 4 different Machine Learning models in Python: ARIMA, Prophet, XGBoost, and LSTM. By splitting the data into a testing and training set, I will compare each model’s performance with one another and conclude which performed best. \n",
    "> \n",
    "Let me know your thoughts, comments, or suggestions below! I'd appreciate it!"
   ]
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    "### **Table of Contents**\n",
    "> \n",
    "This notebook will be broken down into 6 sections:\n",
    "1. EDA\n",
    "2. PROPHET\n",
    "3. ARIMA\n",
    "4. LSTM\n",
    "5. XGBOOST\n",
    "5. CONCLUSION (Comparing each model's RMSE)\n",
    "\n",
    "**Standarized Procedure for Testing Models**\n",
    "- Each model will be trained on the first 70% of the data and tested on the last 30%. The bitcoin data will be resampled to days and restricted to the past 4 years only (from March 31st, 2021).\n",
    "- The Root Mean Square Error (RMSE) of each model will determine which is the best. The RMSE is the standard deviation of the residuals - or more simply how spread out the residuals are.\n"
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   "source": [
    "## **1. Exploratory Data Analysis**\n"
   ]
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    "# import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "from pandas.plotting import autocorrelation_plot"
   ]
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       "      <th>Timestamp</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
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       "      <th>4</th>\n",
       "      <td>1325318160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    Timestamp  Open  High   Low  Close  Volume_(BTC)  Volume_(Currency)  \\\n",
       "0  1325317920  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "1  1325317980   NaN   NaN   NaN    NaN           NaN                NaN   \n",
       "2  1325318040   NaN   NaN   NaN    NaN           NaN                NaN   \n",
       "3  1325318100   NaN   NaN   NaN    NaN           NaN                NaN   \n",
       "4  1325318160   NaN   NaN   NaN    NaN           NaN                NaN   \n",
       "\n",
       "   Weighted_Price  \n",
       "0            4.39  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  "
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   "source": [
    "# read the dataset into a dataframe\n",
    "df = pd.read_csv('/kaggle/input/bitcoin-historical-data/bitstampUSD_1-min_data_2012-01-01_to_2021-03-31.csv')\n",
    "df.head()"
   ]
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
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       "      <th>2011-12-31</th>\n",
       "      <td>4.465000</td>\n",
       "      <td>4.482500</td>\n",
       "      <td>4.465000</td>\n",
       "      <td>4.482500</td>\n",
       "      <td>23.829470</td>\n",
       "      <td>106.330084</td>\n",
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       "      <th>2012-01-01</th>\n",
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       "      <td>5.252500</td>\n",
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       "      <td>5.252500</td>\n",
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       "      <td>5.200000</td>\n",
       "      <td>5.223333</td>\n",
       "      <td>11.914807</td>\n",
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       "                Open      High       Low     Close  Volume_(BTC)  \\\n",
       "Timestamp                                                          \n",
       "2011-12-31  4.465000  4.482500  4.465000  4.482500     23.829470   \n",
       "2012-01-01  4.806667  4.806667  4.806667  4.806667      7.200667   \n",
       "2012-01-02  5.000000  5.000000  5.000000  5.000000     19.048000   \n",
       "2012-01-03  5.252500  5.252500  5.252500  5.252500     11.004660   \n",
       "2012-01-04  5.200000  5.223333  5.200000  5.223333     11.914807   \n",
       "\n",
       "            Volume_(Currency)  Weighted_Price  \n",
       "Timestamp                                      \n",
       "2011-12-31         106.330084        4.471603  \n",
       "2012-01-01          35.259720        4.806667  \n",
       "2012-01-02          95.240000        5.000000  \n",
       "2012-01-03          58.100651        5.252500  \n",
       "2012-01-04          63.119577        5.208159  "
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    "# Here I start cleaning the data. Firstly, converting Timestamp to datetime64\n",
    "df.Timestamp = pd.to_datetime(df.Timestamp, unit='s')\n",
    "# sets the index as the date\n",
    "df.index = df.Timestamp\n",
    "# Resamples the data to the average daily value of each column. Removes excessive frequency\n",
    "df = df.resample('D').mean()\n",
    "# drops any missing values that are present\n",
    "df = df.dropna()\n",
    "df.head()"
   ]
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      "(3376, 7)\n"
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    "print(df.shape)\n",
    "# Now we have 3,376 rows instead of 4 million - with no missing values and accurate datetime information"
   ]
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2OVhJkiRJUsUrjdi/5sTZ/ObeFXs4evQYvXMRJEmSJEljyq1PFndBe9UJszhs2vgyRzN8TOwlSZIkSaPCV294EoBx1RnSo3jqfW8m9pIkSZKkUWVcTYZM2sRekiRJkqSKNK4m7Yi9JEmSJEmVqr4qQzqY2EuSJEmSVJHG17rGXpIkSZKkivLnJ9YBUFuVIpUK3WvsJ9RXcdX7n1vO0Iacib0kSZIkqeJtbOkCoCNb3Ms+lUzFf+kxMzlqVlPZ4hoOJvaSJEmSpIrXUJsB4K2nzwVg8boWAMbCUnsTe0mSJElSxevI5gF486kHArB6awcAq7Z0lC2m4WJiL0mSJEmqeH9+Yj1QXGPfU+/7o9Hof4eSJEmSpFEtxsjv7l8JQG1VeufHyhHQMDOxlyRJkiRVtEKP7L2U2H/lNccUG8ZAZm9iL0mSJEmqaPkemX1tppjm1lWnd3X4qGNiL0mSJEmqaIW4I7HPpItpbqBYDj+OgSF7E3tJkiRJUkWL/eTupW3u+ntstDGxlyRJkiRVtNKI/cFTG/o8ZmIvSZIkSdIIV0rs33DSnO62ZMDeqfiSJEmSJI10hULxeyjNv8ep+JIkSZIkVYzSiH069GwtFc8b/UzsJUmSJEkVrZTYp1KO2EuSJEmSVHFK29j3nIq/8MAJVKUDFz7voDJFNXwy5Q5AkiRJkqR90T1i32Mq/qSGGp76/IvLFNHwcsRekiRJklTRdqyxD3s4cnQysZckSZIkVbTSVPyUib0kSZIkSZWnkGT2YzSvN7GXJEmSJFW2HWvsx2Zmb2IvSZIkSapopan46ZSJvSRJkiRJFac0Yj9GB+xN7CVJkiRJlS06FV+SJEmSpMqVLxS/m9hLkiRJklSBuvexH6MZ7hh925IkSZKk0WLHGntH7CVJkiRJqjhJXu9UfEmSJEmSKlG+UCqeV+ZAysTEXpIkSZJU0QpWxZckSZIkqXIVSlPxx+iQvYm9JEmSJKmi7djHvsyBlImJvSRJkiSpohUsnidJkiRJUuUqFc8bo3m9ib0kSZIkqbKViuelx2hmb2IvSZIkSapo2XwBgEx6bKa4Y/NdS5IkSZJGjVy+OGJflXbEXpIkSZKkEakzl+9eS99brlAcsU+P0bL4Q57YhxDSIYT7QwhXJffnhRD+GkJYHEL4ZQihOmmvSe4vTh6f2+M5Ppa0PxFCOK9H+/lJ2+IQwkeH+r1IkiRJksrjsE/8kX/8yb39PpYrlEbsx+bY9XC86w8Aj/W4/5/A12KMBwObgXck7e8ANiftX0uOI4RwBPAG4EjgfOCbycWCNPC/wIuAI4A3JsdKkiRJkkaRQpK43/jY2n4fL03Fzzhiv/+FEGYDLwG+n9wPwNnAb5JDLgNekdx+eXKf5PFzkuNfDvwixtgZY1wCLAZOTr4WxxifiTF2Ab9IjpUkSZIkjSLbOrK7fbxUPM8R+6HxdeDDQCG5PwnYEmPMJfdXALOS27OA5QDJ41uT47vbe52zq/Y+QggXhhAWhRAWrV+/fh/fkiRJkiRpOG1q7drt4x/6zUMAZCyet3+FEF4KrIsx9r8IYhjFGL8bY1wYY1w4ZcqUcocjSZIkSRqEjmxhzwcBmdTYHLHPDOFzPwf4uxDCi4FaoBH4BtAcQsgko/KzgZXJ8SuBOcCKEEIGaAI29mgv6XnOrtolSZIkSaNEqer9nrjd3X4WY/xYjHF2jHEuxeJ3N8cY3wT8CXhNctgFwB+S21cm90kevznGGJP2NyRV8+cBhwB3A/cAhyRV9quT17hyqN6PJEmSJKk8svn+t7nrLTNG19gP5Yj9rnwE+EUI4XPA/cAPkvYfAD8JISwGNlFM1IkxPhJC+BXwKJAD3htjzAOEEN4HXAekgUtjjI8M6zuRJEmSJA25XH6gU/HH5oj9sCT2McY/A39Obj9DsaJ972M6gNfu4vzPA5/vp/0a4Jr9GKokSZIkaYTJFwY2Ym9VfEmSJEmSRqBsj8S+I5vf5XHpMTpib2IvSZIkSRrRek7FX/Aff+zzeH11mnMWTB3OkEYUE3tJkiRJ0oi2p+J56RA4YFL9MEUz8pjYS5IkSZJGtD1td5ctFKgeo+vrwcRekiRJkjTC5XqN2Peukp/NRzJjdA97MLGXJEmSJI1w2V6JfGvnjgJ6hUIkX4hjtiI+mNhLkiRJkka4XK/t7lq6ct23H1m1DYBpjbXDGtNIYmIvSZIkSRrRek+9b+3ckdiv3NIOwDGzm4Y1ppHExF6SJEmSNKL1rorf0iOxLyX5DTWZYY1pJDGxlyRJkiSNaPleU/GvfXh19+3WZFp+fbWJvSRJkiRJI1K213Z337ttCQ+t2ALsKKTniL0kSZIkSSNU7+3uYEdC39qZIxWgtmrsprdj951LkiRJkipC7+J5ANWZYjrbmctTW5UmBPexlyRJkiRpRMoW+o7YF2KxrStX6E7yx6qx/e4lSZIkSSNefyP2ndliW1e+QHV6bKe2Y/vdS5IkSZJGvN7b3QF05Ytr7DtzBapM7CVJkiRJGrlyhV2P2GfzkRqn4kuSJEmSNHL1VxW/K5me35XLO2Jf7gAkSZIkSdqdXI/ieYdOawCKU/DB4nlgYi9JkiRJGuF6Fs+ryaSBYmL/xWse409PrDexL3cAkiRJkiTtTs/t7tZt7wCKI/XfufUZAKrSY3cPexhkYh9CmBBCOGaogpEkSZIkqbeeI/Zrt3UC0JnLd7dVJ6P4Y9UeE/sQwp9DCI0hhInAfcD3QggXD31okiRJkiTtXDzvHc+dBxRH7Evqq0zs96QpxrgNeBXw4xjjKcALhjYsSZIkSZKKek7FP/eIaVSlQ3fxPID6GhP7PcmEEGYArwOuGuJ4JEmSJEnaSS5fYHJDDW865QAWHjiB6nRqpxH7cdWZMkZXfgNJ7D8DXAcsjjHeE0I4CHhqaMOSJEmSJKkol48cPHUcn3/l0WTSKaozOyf26dTYLp6328saIYQ0MCfG2F0wL8b4DPDqoQ5MkiRJkiSAbKFAQ9WO9LUmk96peF4hxv5OGzN2O2IfY8wDbxymWCRJkiRJ2snW9iz3L9tCpseofO8R+/oxPhV/IO/+LyGE/wF+CbSWGmOM9w1ZVJIkSZIkAe+//H4ANrdlu9tqMil+/8Cq7vsvOmr6sMc1kgwksT8u+f7ZHm0ROHu/RyNJkiRJUg9LNrQAkO2xl311Zsfk87ecdiDHzmke7rBGlD0m9jHGs4YjEEmSJEmSekuH4hT8VNh5Kn5JXfXY3uoOBjZiTwjhJcCRQG2pLcb42V2fIUmSJEnSvkulSon9jraaHol9ZoxXxIcBbHcXQvg28Hrg/UAAXgscOMRxSZIkSZLUPWJf6FH4vjqzY5Q+nRrILu6j20B64PQY41uAzTHGzwCnAYcObViSJEmSJO3Yoz7fI7Nv7czteDw4Yj+QxL49+d4WQpgJZIEZQxeSJEmSJElFqe4R+x2J/b1LN3ffzqRN7Aeyxv6qEEIz8BXgPooV8b8/lEFJkiRJkgTQlVTDb6qr6vfxtGvsB1QV/6Lk5m9DCFcBtTHGrUMbliRJkiRJ0N6VB2D+1IZ+Hzet301iH0J41W4eI8Z4xdCEJEmSJElS0XMOnsSvFq3gEy85vLvtxg8+n3/8ySKeXt9K3M25Y8XuRuxftpvHImBiL0mSJEkaUvkCzGquo756R/p68NQGnnvwZJ5e37rT1ndj1S4T+xjj24YzEEmSJEmSessXCv0WyOvMFdfeV5vY73Yq/gd3d2KM8eL9H44kSZIkSTtkC7HfAnkLpo8H4OAp/a+9H0t2NxV//LBFIUmSJElSP3L5AlWpvqPyF5w+l5PnTeKImY1liGpk2d1U/M8MZyCSJEmSJPWWy/c/Yh9CMKlP7HExQghhdgjhdyGEdcnXb0MIswdwXm0I4e4QwoMhhEdCCJ9J2ueFEP4aQlgcQvhlCKE6aa9J7i9OHp/b47k+lrQ/EUI4r0f7+Unb4hDCR/eqByRJkiRJI1ZXvkBNlevod2cgvfND4EpgZvL1f0nbnnQCZ8cYjwWOA84PIZwK/CfwtRjjwcBm4B3J8e8ANiftX0uOI4RwBPAG4EjgfOCbIYR0CCEN/C/wIuAI4I3JsZIkSZKkUaIjm6c2ky53GCPaQBL7KTHGH8YYc8nXj4ApezopFrUkd6uSrwicDfwmab8MeEVy++XJfZLHzwkhhKT9FzHGzhjjEmAxcHLytTjG+EyMsQv4RXKsJEmSJGmU6Mw5Yr8nA+mdjSGEN5dGyUMIbwY2DuTJk+MfANYBNwBPA1tijLnkkBXArOT2LGA5QPL4VmBSz/Ze5+yqXZIkSZI0SnRmC+5VvwcD6Z23A68D1gCrgdcAA9rjPsaYjzEeB8ymOMK+YO/C3DchhAtDCItCCIvWr19fjhAkSZIkSXuhI5entsqp+Luzu+3uAIgxLgX+bl9eJMa4JYTwJ+A0oDmEkElG5WcDK5PDVgJzgBUhhAzQRHFmQKm9pOc5u2rv/frfBb4LsHDhwrgv70WSJEmSNHwcsd+z3fZOCOGsEMIVSVX7R0IIvwkhnDmQJw4hTAkhNCe364BzgceAP1Ec9Qe4APhDcvvK5D7J4zfHGGPS/oakav484BDgbuAe4JCkyn41xQJ7Vw4kNkmSJElSZejI5amxeN5u7XLEPoTwEuB/gM8CnwECcAJwaQjhfTHGa/bw3DOAy5Lq9SngVzHGq0IIjwK/CCF8Drgf+EFy/A+An4QQFgObKCbqxBgfCSH8CngUyAHvjTHmkxjfB1wHpIFLY4yPDLoHJEmSJEkjUoyRts489dUm9ruzu6n4HwJeEWN8sEfbAyGERcB/A7tN7GOMDwHH99P+DMX19r3bO4DX7uK5Pg98vp/2a/YUhyRJkiSpMm1s7aIrX2BGU225QxnRdjcVf3qvpB7oTtinDV1IkiRJkiTB6i0dAMxoritzJCPb7hL71r18TJIkSZKkfbalvQuAieOqyxzJyLa7qfjzQwj9FaMLwEFDFI8kSZIkSQBsbc8C0FhbVeZIRrbdJfYv381j/7W/A5EkSZIkqadt7TkAmupM7Hdnl4l9jPGW4QxEkiRJkqSetncUR+zH1+5uTFq73cdekiRJkqRyac/mAaircru73TGxlyRJkiSNSJ25AtXpFKlUKHcoI9qgEvsQwoQQgj0qSZIkSRpyHdk8NRnHo/dklz0UQvhkCGFBcrsmhPAn4GlgbQjhBcMVoCRJkiRpbOrMFahxGv4e7e7Sx+uBJ5LbFyTfpwDPB74wlEFJkiRJksa2fCHym3tXlDuMirC7xL4rxhiT2+cBv4gx5mOMj7H7bfIkSZIkSdonDyzfTFeuwIaWznKHMuLtLrHvDCEcFUKYApwFXN/jsfqhDUuSJEmSNJa1dxXKHULF2N3I+78Av6E4/f5rMcYlACGEFwP3D31okiRJkqSxqjNX3OpuXLVr7Pdkl4l9jPEuYEE/7dcA1wxlUJIkSZKksa0zVxyxv+I9zylzJCPfLhP7EMIHezVFYANwe2n0XpIkSZKkoVAasXe7uz3bXQ+N7/XVCCwErg0hvGEYYpMkSZIkjVGd2eKIfU2Vif2e7G4q/mf6aw8hTARuBH4xVEFJkiRJksa2jmxxxL424xr7PRn0pY8Y4yYgDEEskiRJkiQBO9bYO2K/Z4PuoRDCWcDmIYhFkiRJkiRgR2JfnTax35PdFc97mGLBvJ4mAquAtwxlUJIkSZKksa0zlyeTCmRM7Pdod/vYv7TX/QhsjDG2DmE8kiRJkiTRmS1YEX+Adlc8b+lwBiJJkiRJUklnrkBNlYXzBsLLH5IkSZKkEacjm3fEfoDsJUmSJEnSiNOZcyr+QNlLkiRJkqQRpzOXp8Y97AfExF6SJEmSNOIU19ibsg6EvSRJkiRJGnHau/LUOmI/ICb2kiRJkqQRZXNrF1vasjTW7W6HdpXYS5IkSZKkEaMjm+f4i24AYFJDdZmjqQyO2EuSJEmSRoxHV2/rvn3H0xvLGEnlMLGXJEmSJI0YKza3d98+ed7EMkZSOUzsJUmSJEkjxrptHQDUVqX40dtOKnM0lcE19pIkSZKkEWP5pjbqq9M88pnzCCGUO5yK4Ii9JEmSJGnEeHJtC4dOG29SPwgm9pIkSZKkEaOlM0dzfVW5w6goJvaSJEmSpBEjmy+QSZmqDoa9JUmSJEkaMfKFSFXaafiDYWIvSZIkSRoxcoVIJm2qOhj2liRJkiRpxMjmC1SlHLEfDBN7SZIkSdKIkctHMk7FHxQTe0mSJEnSiJErFJyKP0j2liRJkiRpxMjmIxmn4g+Kib0kSZIkacTIF6Lb3Q2SvSVJkiRJGjGy+YLb3Q3SkCX2IYQ5IYQ/hRAeDSE8EkL4QNI+MYRwQwjhqeT7hKQ9hBAuCSEsDiE8FEI4ocdzXZAc/1QI4YIe7SeGEB5OzrkkhOC/viRJkiRVsOJ2d6Z2gzGUI/Y54N9ijEcApwLvDSEcAXwUuCnGeAhwU3If4EXAIcnXhcC3oHghAPgUcApwMvCp0sWA5Jh39Tjv/CF8P5IkSZKkIRRjdCr+Xhiy3ooxro4x3pfc3g48BswCXg5clhx2GfCK5PbLgR/HoruA5hDCDOA84IYY46YY42bgBuD85LHGGONdMcYI/LjHc0mSJEmSKkw2HwGcij9Iw3IZJIQwFzge+CswLca4OnloDTAtuT0LWN7jtBVJ2+7aV/TTLkmSJEmqQB25PAA1mXSZI6ksQ57YhxAagN8C/xJj3NbzsWSkPQ5DDBeGEBaFEBatX79+qF9OkiRJkrQXtrZlAWiqqypzJJVlSBP7EEIVxaT+ZzHGK5Lmtck0epLv65L2lcCcHqfPTtp21z67n/Y+YozfjTEujDEunDJlyr69KUmSJEnSkNjaXkzsG03sB2Uoq+IH4AfAYzHGi3s8dCVQqmx/AfCHHu1vSarjnwpsTabsXwe8MIQwISma90LguuSxbSGEU5PXekuP55IkSZIkVYBVW9q56bG1AGxrd8R+b2SG8LmfA/wD8HAI4YGk7ePAl4BfhRDeASwFXpc8dg3wYmAx0Aa8DSDGuCmEcBFwT3LcZ2OMm5Lb7wF+BNQB1yZfkiRJGmIPrdjC569+jMvefjK1Va6FlTR4MUa2d+Y4/Us3d7e947nzADhwUn25wqpIQ5bYxxhvB3ZVyvCcfo6PwHt38VyXApf2074IOGofwpQkSdJe+NSVj3D/si08tGIrJ8+bWO5wJFWg/7l5MV+94cmd2n5w+xIWTB/PzOa6MkVVmdwcUJIkSYM2vrY4TbY0bVaSBqMzl++T1JccN6d5eIMZBUzsJUmSNGiNtcWJnxtaOssciaRK9MVrHt/p/qdfdgTpVHHC98Rx1eUIqaKZ2EuSJGnQxlUXE/vVWzvKHImkSvTQii073Z8wrpri6mwT+71hYi9JkqRB68zlAVi33cRe0uBNb6rd6f6E+moKxbyeSQ0m9oNlYi9JkqRBa+ksJvbtXfkyRyKpEm3Y3rXT/Qn11UxOEvp0yjR1sOwxSZIkDVprZw6AzlyhzJFIqjQbWzq5+9lN1Ffv2Cqzub6KD557GACzJ1gRf7CGch97SZIkjVKtXSb2kvbOsxvbADhyZiP3PLsZKK6xf+PJczh9/iTmTh5XzvAqkiP2kiRJGrSWZMS+I+tUfEmDU9om87UL53S3jatOE0Iwqd9LJvaSJEkaNKfiS9pb2zqKif30xh0F9EII5QpnVDCxlyRJ0qC1JsXzStXxJWmgSiP2B09tKHMko4eJvSRJkgYlxti9xr4j64i9pMHZmiT2pW3tDpxUX85wRgWL50mSJGlQvnfbM8Rkv+me2911ZPP8/v6VvObE2WTSjh9J6t+2jhy1VSlqMmnu/NjZjKsxLd1X/saVJEnSoHzhmscBSAXY3LZjL+rf3reCj17xMD+649kyRSapEmxty9JYWwXAjKa67tvaeyb2kiRJ2iuFCG1d+e7K+IFi8avF61rKGZakEWzd9g5+uWg5jXUm8/uTib0kSZIGpb46DcAHzjkE2DFqn80X19unUla3ltS/f/nFAwAUCrG8gYwyJvaSJEkatHedMY85E4sFr7qSLe9Ke9un3bZK0i6s2dYBWBF/fzOxlyRJ0qBk8wWq0imq0iG5Xxx5K+1tn4+OxEnqX1MyBf//veTwMkcyulh+UJIkSQMWYySbj0liXxwj+uafFvPCI6d3j9h3dLm3vaSdbW3L8vHfPcz9y7bw5lMP4MBJ48od0qjiiL0kSZIG7N9+/SAA1ZkUmWQt/RX3r+TdP723O7Fvz5rYS9rZjY+t5eqHVwNw0GSn4e9vJvaSJEkasCvuWwlAVTp0j9iXlKbilxJ8SSqpS4puAiycO6GMkYxOJvaSJEkatKp0ikx65yJ51z2yFoC1SXEsSSrZ3pEF4B+fdxDHzG4ubzCjkIm9JEmSBi2dCmRS/f8puXqLib2knW3vKM7kec9ZB5c5ktHJxF6SJEmD1taV766K39v2zhydOdfZS9qhlNg31Fi/fSiY2EuSJGnQWjtzZNJ9/5R87sGTAdjU2jXcIUkawVo6c4yrTpNO9X9BUPvGxF6SJEkDUijs2J++pTPXXRW/p9kT6gD4zi3PDFtckka+7R1ZxtdWlTuMUcvEXpIkSQOSLRS6b09vrCXGvsfMmVgPwI/ueJauXKHvAZLGpO0dORpqnYY/VEzsJUmSNCDZfDGTP+OQybzzjIPoyvddR18asQd4cu32YYtN0siVyxdYtaWd8Sb2Q8bEXpIkSQOSyxdH4M9eMJV0KtBQ03da7UGTG7pvv+dn9w1bbJJGrn/8yb08uGIrk8bVlDuUUcvEXpIkSQPSlST2VUnRvMOmj+fMw6bsVOX6kGkNnHFIsYDewrkThj9ISSPKyi3t3PT4OgCOntVU5mhGLxN7SZIkDUgumYrfc5u7H73tZK79wBnd92ur0vzkHacwvjZDU52FsqSx7qWX3AbAa0+czfvPdg/7oWJiL0mSpAHJ9hqxL2lMKl2/4PBp3W01mRSdFs+TxrTWzhyb27IAfPJlR5Byq7shY/UCSZIkDciuEvum+iqu+5fnMX/KuO62mkyazqyJvTRWFQqRl/3P7QB8+80nuNXdEHPEXpIkSQOS7Wcqfslh08eT6ZHwF0fs+1bN19jWmcuzubWr+/7abR10ZP2cjEZrtnXwzPpWzlkwlfOPmlHucEY9E3tJkiQNSGnEPpPa85+Q1ZkUK7e0s3Zbx1CHpQryTz+9j+MvugGAbR1ZTvnCTXz2qkfLHJWGwsaW4gWcN5x8QJkjGRtM7CVJkjQg7V3FkdX66vQej62pSnP/si2c8oWbhjosVZCbk+ro+ULkbyu3AnDXMxvLGZKGyIaWTgAmNVSXOZKxwcRekiRJA9LSmQNgXM2eyzTVZPwzU7vW0pFja1JUbSAXijT8bn58LT+9a+len78+SeynNLh3/XCweJ4kSZIGxMRe+8u2jixb24uJfe9ijCqvza1dZPMF3v6jRQC8+dQDgWKF+/rqNCEMrLJ9acR+son9sPCnSJIkSQNSSuzH1w4ksd8xCmsRPfX2gotv4fJ7lgP9J/aFQuQdP7qHs7/65+6RfQ29v63cyvEX3cDJPZbQXHzDkyzf1MaRn7qOS//y7ICfa/32TsZVp6lzRsawMLGXJEnSgLR07N2I/Yu+cRsnf/7GPsd05vLdBfk6snmWb2rbT5FqpJrVXAdAZ67Ag8u3AHD3kk3EGHc67rbFG7jp8XU8s76VO57eMNxhjklfvOYxXvrft/dpv+Smp3jJJbcB8Pv7Vw74+Rava2Fejy0wNbRM7CVJkjQgSze10VRXxbgBjMA9s6F1x+31razb3tnnmMM+8UdeliQSZ37lz5zx5T+RL8Q+x2n0mNFU22/7E2u373R/1Zb27ttf+uPjQxqTYHtHlu/c+sxObecfOZ1/PvtgALYlF/UGUgjvB7cv4eTP38htT23g2NnN+z1W9c819pIkSdqjGCPXP7KWw2eMH9Aa240tfRP51s5cn9H+x9dsp70rz5pkW7zNbV2uyR3FWrv6X5bx5NoWFkxv7L7fPTukOs3SjW20d+Wd0j2EVm/dsS3ll151NJ25Aq84fhZNdVVMbazlk3/4G411VWxu7drt86zY3MZFyfaF6VTgVSfMHtK4tYMj9pIkSdqj9S2dbGjp5Lwjpw/o+K5kin1Pm3okBT2nXi/rMQW/tPe1Rqf1vWZu/PM5h/Tbvr0jSwjw5dccC8DT61uGJ8Ax6u+/91cADp/RyAuPnM4Fp8+lqa4KKBbP+9tnzuPMQ6ewscfP8OJ1LXz/tmd2+ll+bHVx5sVv/+k07vvEuZx44IRhfBdjm4m9JEmS9mj5puLU6LmTBrZmtivXN7EvVUGHnUdun924Y9r+hn5G+jU6dObybGjpZO6kegAm1Ffxry84hOp0qk9iv2ZbBw3VGaYnU/fXbO3odxaI9l1HNt/9c3f1+5/LxHF9p9vXV2eYOK6m++Lck2u384KLb+FzVz/GDY+u5SWX3MYtT65n8briBZhDpo2nqb5q+N6ETOwlSZK0Z1vbi3/QT+jnj/7+9Lfd3ZYe1c2vfGBV9+17lmzqvm1iP3p995biGu7nHjIZgJnNdYQQmDK+hhseXcOFP15ER7Z4wecvizcyf2oDk5M13e/88SJO/NyN3aPDa7Z28MFfPcCaHlPItXdKyfoXXnk0qdSul9lMaqimrStPRza/UxG9C39yL4+s2sYFl97Nfyb1EBprTeqH25Al9iGES0MI60IIf+vRNjGEcEMI4ank+4SkPYQQLgkhLA4hPBRCOKHHORckxz8VQrigR/uJIYSHk3MuCQPdUFGSJEmDdsOjawG6p+fuyS8uPI0Pn38Yl7/rVH76jlMA2NK+YxrvoqU7kvnv376E+Un1bKfij17LN7dRV5XmtSfOAYo1FwCmjK/h6fWtXP/oWq58YBVtXTlWbmnnBYdPZVKvegvb2ovnvPx/b+eK+1byhu/eObxvYpAKFVAMspTY76kwXmkkf1NrF/c8u4kF08czoZ9R+Q8kyys0vIZyxP5HwPm92j4K3BRjPAS4KbkP8CLgkOTrQuBbULwQAHwKOAU4GfhU6WJAcsy7epzX+7UkSZK0H3TlClx+d3HP8cYB7GEPcNj08bznzIM5bf4kDpnWAOw8Yr+l197kb3vOPFJh53X4Gl1aO/PMmlDH7Al1O7WXpuYD3PnMRp5ZX1yaMX9KQ58dGI797PVcfP0TrN1WnNnx7MY2Dv74NXz6yke6LxSMFN+48SlO+vyNrOxR4X8kKq2bn7SH2TilZRGnf+lm7nl2My88cjr/+Pz5zGiq5eZ/ez7/9dpjufNjZ/Ov5x465DGrryFL7GOMtwKbejW/HLgsuX0Z8Ioe7T+ORXcBzSGEGcB5wA0xxk0xxs3ADcD5yWONMca7YnE+zo97PJckSZL2o/U9psc3DnDEvqfSKH/PNfab27qoTu/4U3TSuGoa66rY1pHtc75Gh+3JrggTx1XzofMO43tvWQjASfMmdh9z3SNrutdpz5/a0O8ODJfcvBiA15xYrLieK0R+dMezHPmp60bMdolrtnbwtRufZGNrF8/50s1c8/Dqcoe0S6XaBb1nR/R2xsGTmTe5OLOmOp3i/WcfzLufP587P3YOB01p4DUnzmZGU91un0NDZ7jX2E+LMZY+1WuAacntWcDyHsetSNp2176in3ZJkiTtZ+u27VjHXJUe/J+PtVVp6qrSbGnbMRq/ZmsHZyRrrQGa66tpqqvaKfkfjGfWt3hRYIRr7czRUJMmhMB7zzqYQ6aNB3Zej93Wledff/UAqQAHJiP56VTY6SJQyVmHTeWcBVP5xEsO7267/O5lQ/wuBqa01rzkPT+7j/uWbd4vz/2re5Zz8fVP9Glv68rxv39aTDZf6K5VMBClWTL9Fc3rKZNO8eO3n8znXnEUD3zq3L36XaChU7Z/jWSkfVguqYUQLgwhLAohLFq/fv1wvKQkSdKosS6pWP68Q6fs9XM011d1T7/f1NrF6q0dnHrQpO7HpzfV0lhbxba9SOxz+QJnf/UW/uH7f93r+DT0tndkaajpu5Sj96B8jDBnYj01meI0/Ps/eS73ffJcnv3SS/iv1x7bfdyciXX84K0n8c4zDuJP/34mzfVV/OnxdUP6HgbqgeVbADj+gObutld984798twf/u1DXHLzYra2Zfnobx/i6oeK46b/ffNivnLdExzy/65lwX/8kQeTGJZsaN3tRa+VW9qprUoNaJnNnIn1vPnUA6mvHtiSHA2f4U7s1ybT6Em+l37yVgJzehw3O2nbXfvsftr7FWP8boxxYYxx4ZQpe/8fkiRJ0lhU2orsK685Zq+fo6muii3tOxJ7gKmNNRyUFM2bM6GOproqVm5pZ2vb4JL7p5M12Q+u2LrX8Wnord3WydTxtX3aSwn86fN3XOg5YOKOdfeNtVXdFwSmjN8xXbw0Lbx0+7g5zazdvvsq+Xcv2TTklfRXbWlnyYZWPnL+Ai55w/E7PbZ5P9aQOPaz1/OLe5bz3p/fRzZf4JYndh7A/NdfPcD1j6zhrP/6M+/+yb39PsearR38/v6VHDu7ud9lD6ocw53YXwmUKttfAPyhR/tbkur4pwJbkyn71wEvDCFMSIrmvRC4LnlsWwjh1KQa/lt6PJckSZL2o3XbOwlhz8W1dqe5vqo7YS+NHjbWVfHLC0/jN+8+jUw6RWNdhifXtnDsZ68f0HPe+uR6vnjtY5z39Vu72+54esNex6ihs257B1vbs90F2Ho6Z8FUvvSqo7n0rSfxk3eczPjaDG865YB+n+eYWU089+DJXPLG4xnfa0u1aeNru4vq9eeJNdt53Xfu5Ln/eTNvufRu/vdPi/ftTfXS3pXn1d+6g9O/dDMAzz90CnMm1nPrh87igtMOBOCppH7A3srmC/22//jOpTy6ehsNNRm+8YbjOGzaeJ5Z38qFSUJ/x9MbgWKdi/f+/D4+d9WjPLZ6Gz+9aymb27L8+3mH7VNcKr8hm0MRQrgcOBOYHEJYQbG6/ZeAX4UQ3gEsBV6XHH4N8GJgMdAGvA0gxrgphHARcE9y3GdjjKWCfO+hWHm/Drg2+ZIkSdJ+tn57B5PG1ZDZhzW1zXXVPLOhmNSUpts31maYMr6mexS2VA0dIMa4xxHEt1x6d5+2v//eX3n2Sy/Z6zi1/8UYOfnzNwFw8NSGPo+nUoE3nFxM5M84ZAoPfeqFu/y3nzCump++85R+H2usy9DSsevK+LcvLl70yRUitz65nlufXM/LjpnJAT2q8u+Le5du5t6lO9bRL5herCFwwKR63nTqgVx251LWbOvgkVVbyRcix8xuHvRrLN3YutP90w6axJ3PbOSiqx5lemMtd37sbEIIzJ/SwEv/+/adjp370at3uv/925cAcPaCqZw0dyKqbEOW2McY37iLh87p59gIvHcXz3MpcGk/7YuAo/YlRkmSJO3Zum2dTB2/+4rZe9JUV8WTa1voyOa7C+Q19hpxXbF5x7Zgtzy5nh/d8SzfeP3xNPWzV3bxz8ed9VzHr5Gj579rz+n2u7K3U8LH1WRoz+bJFyLpVN/nKBWUO/OwKWxo6eRvK7fxk7ue5f+95Ii9er2Se5duZv6UcTy1bjsAP377yWRSgVSPGEozFZZvauOfL78fgMWff9GgLpYt39TGCy6+dae2i15xZHfbIdN27CJw1Kwmbv3QWUwZX0NXvsAr//cvPLOheFHg2NlNvPDI6fz2vhVMaajh4y8+HFU+qx5IkiRpt9Zt72Rq474l9kuSkcaLrnqUWck+5lMbd56W/c/nHMwXrilWE3/rD4sTNo/97PX9jsD/+t4dGyQ979ApfOiFh3Hz4+v42o1P0pUrUJ2xYnc5bG3P8qFfP8iFzzuIBTMaaajJsGxTGwCXv+vUPtPn96fSOvzWrlyfi0YAnbniNPYfvvUkYoSDPn4N37ttCSceOIHzj5qxV6/5jRuf4ms3PsnhMxo5bk4T42sznHHI5D4XJxprq5jZVMtXrttRzX5TW1e/NQd25Ywv/6lP20GTG/jiq47mY1c8zFGzmnZ6rDQToY40v3vvc2jtLM5maKqrYlxNhveedfCAX1sjn7/xJEmStFvrt3cyZQ97XO9JabT0Z39dxpf/WExuelfhvvB587n2A2f0Obcr13dd8X1LNzOuOs09/+8F/PjtJ3P07Kbuiw+HfuLavd42T/vmsdXbuP7Rtbzm23dy1KeuI8bI8iSxnzNxaPc4H5ck9m2dxc/a4nUt/PjOZ7sf78zmqcmkCKE4mv7plxVH6t/90/t4dkNrn+fbkxgjX7vxSaD4vi+/ezkHT23Y5YyDc4+YttP9TXtZSK9Uf+DUgyaSSgVecdwsPveKo/jnsw/Z5TlNdVXMbK5jZnNddz9pdDGxlyRJ0i7lC5ENLfs+Yv/RFy3o09ZfAjShvm+Bvt7riottbRw6ffwuq6Qf+5nr+dK1j/c5T0NrS9vOyer67Z0s29RGJhWY0TQ8if1VD63i7iWbuODSu/nkHx7p3tu+M1egtirdffxbnzOP37z7NAD+/nt3Dfr1tiXr+c9eMLW7rednsLePvfhwjj+gmbc/Zx4Am1oGl9hPa6zhtSfO5vOvPJrbP3IWP3lHsdZAXXWaN596IHXV6T08g0YzE3tJkiTt0ueufpRcITJvct+iZ4Nx+vzJHDN7x1Th4+Y093tcc4/19CfPKxb06llJPF+IfOjXD3LnMxtZML1xp3MXHjiBlx07s/v+t295eq/jfWLNduZ+9GrO/uqfWbttaLdHG00296pxsKU9y0MrtjJv8rh+173vT4dOK35GP3f1Y7zuO3eycktxbf/HrniYj13xMD+649k+SzQWzp3Icw6exKqtHbR17brwXm9XP7Sas//rzwC84vhZfPW1xwLw4t1M6a+tSvO79zyH159U3M174yBG7GOMbGrtYlIyc2b2hHqq9qGYpUYfPw2SJEnq1z3PbuKHf3kWgFPm7XvV7Loeo6Wf+bsj+z2m54jqUTObCAEeX7O9u+3mx9d1r6/vHVMmneK/33g845OR2xD6L7I3ENc9sgYoVuq/9C9L9uo5xooHl2/hpf99Gxff8CSX3fEs1ekUP7hgIQA//+sybl+8gXMOn7aHZ9l3h04d36ft3849FKB71L6/feT/8XnzAfhNj7oNe/Len9/XnZjPnlDHq0+czd8+cx4vOGLP73NSQ3FWysMrtw749bZ35sjm4z5tOanRzcRekiRJfRQKkdd++04AHvjkucyZuO9bgm1Ppi5/8VVHc+wuRuwB3nr63GIMMXLI1AaufXh192O3PrkegFedMIvzj5re7/m3feQs3nr6XGJkr6vkP9tj+v8Dy7bs1XOMFX94YBV/W7mNS256isfXbOf1J81hWlIY8Ud3PAvAeUcOfWKfSgUuevmRnHXYFD76ogUcM7uJt5w+l38+Z8fa81yh74We5x06hcbaDPf12KpuoA6d1sDxyWe5YYBr10vLTb576zMU+omnP6Vp+xNN7LULJvaSJEnqY9XWHVuUNfez7n1vFJLR8+a63VdGP/6AZqC4dvjsBdN4dmNrdwK0ua2LeZPHcfHrjttpdL+n5vpqTku2VXt2Yys/uH0Jcz96NQs/dwN/G+Ao6YpN7Zw0dwKvPmE2f12yiVy+bwE/FW3uta7+0OnjOXhqQ3cS+qoTZu1y6cX+9g+nzeWHbzuZdz9/Ple+77k01VXxwXMP5fp/fd5uzztqVhMrNrdzzcOr+X+/e5iObJ7OXH6Xx4+vzZBJBX7+rlMHvT1fOhW6P+OtA5z+f/k9xRkHpW3zpN4siShJkiSgWLn+Q795iJPnTmDBjOL69Y/1U/Rub5USoD0V+XrpMTNZv72Tvz/lAH5730qy+cj6lk6mNdaypS270zr8XTk62frrld+8o7ttQ0sXVz64qs+2YL39etFy7n52E2865QBmJInUtX9bs9P6/Z4KhbjTnuVjTc+14p9+2RG88aQ5ZNIpbv/IWeQLcUi3uBuouZN2XdQOYMr4Gu5ftoX3/Ow+oDhNfsn6Vq765+dy4KRxfOnax7nlyfVc/q5TaOvKs70jx0dftIDJe7lbxKuOn8X9y7bQkS3Q3453Mcbun5cn1mzn+7ct4ZR5EzntoEl79Xoa/RyxlyRJEgBXPrCK/3twFf/Ro5L4/lwbXcp997TdVjoVeOcZB1FfnWFKsh75lC/cxG/vXcHmtq49jvgDzGyu4yXH7Chk9rbnzCUEWL1194XwNrV28aHfPATAGYdM4cJk/fXdSzbt8vhzLr6F//j93/YY02i1cnNb9+2/P+VAMklRt/rqzIhI6gGqMyne/fz5XPb2k/t9fHpjLcs27XgfD63YyvbOHOd//TY2tnTy7Vue5rHV2zjuszdw+pduJhXgJUfvulDenpRmm5S2gezpO7c8zWGf+CN/emIdrZ05fv/ASvKFyBdedfSYvoCk3XPEXpIkSQDc+Nja7ts3PFK8PbN5/039Lc1YHkw1757b333ktw8Rwo7R+D35wiuPJpsr8JoTZ/PCI6fz6KptrOmxxKA/67YXE//3nXVw9xr+5x06hSvuW8HHXryA+uodfz6v2drB2V/9M21deZZsaOWiVxw14Pc1WnTmiu99Qn0VFz5vfp+q8yNJf1sulrz8uFl859ZnAHjPmfPpyBbY0NLJlQ+u4sTP3djn+AMm1u9T3YnSrJX2fhL7Pz2xjq58gVufXM/Xb3iSB1dsJQQ4aDdb6Ukm9pIkSQJg2aY2muur2NKWZXtnjgn1VTslsvsqlWT2g6lU33N9f6nwWakw25401VXx3bcs7L4/s7mOu57ZuNtz1m/vBIrJfMkZB0/m1ifX880/Pc2/n3dYd/snfv8wbV3FxCwVxuaU/DVbOyhE+PiLD+e1C+eUO5y9dsTMHVsnfvj84gWArlyBhtoMP/9rcfbKI585j1w+8rHfPcS7nz9/n16vNtP/iP2m1i7ueqY4O6S0IwVAjAx6Lb/GlpF7SU2SJEnDJsbIis3tvOK4WZyQFPaa2Vy3X1/jzaceCBRHOwdqXj+jlAMdse9tWmMtq7d28LO/LmXuR6/mV4uWA7ClrYuLr3+CV3/rDp5e1wIUp2aXvP2588ikQve+6AAPr9jKjY+tA4rT/AsRWpJCaM9uaOWCS+/mi9c+xh8eWEmMkY0tnQOugF5JVm0pznDY35+VcrjlQ2dy24fP6r5fnUnxjufOA+Dvjp3JuJoMTfVVfPNNJ3LM7OZ9eq3uEfuuPIVC5H//tJhP/P5h3nHZPQAcOGnHz8gp8yZy6VsX9vs8Uokj9pIkSWJLW5aWzhxzJtaztT3Lfcu27Pdk7XUL5/C6QY7qVmdSPPrZ87jh0bV84BcP8D9/fzznHD51r17/3COm8e1bnub//a64Hv6X9yznlcfP4kXfuK177f3W9izpVNhpCUI6FThyVhNLN7ayubWLT//fI/zhgVUA/Pcbj+8edb37mU389r4V3L9sC2u2dXBLsjXfB37xAAAvOWYGbz19Lptau6jJpJg9oY75Uxr4y+KN3PDoGj7yogX7dYbEcFizrXixYzRUaz+wnwJ786c08Nt/On1QF6MGorTGvj2b58l12/nKdU90PzZnYh03/9uZXPXQKg6dNp7DZzTu6mmkbpX1m0OSJElD4qlkpPqgyeNYlYxMHzqtoZwhdauvzvDy42bx8uNm7dPz9N5ybc3WDv7j939j9dYODpxUz9KNbSxe18KC6eO7C8CVHDixnisfXMXxF92wo21SPS87dmb39P53/nhRn9cMoTiNGuDqh1Zz9UOrdxnf/z20mu+9ZSEnHjhhL9/h8CuN2M8YBYn9rgzFv0ddktiv3trBW394z06PHTu7mXQq7PPnXWOLib0kSdIYdveSTcydVM9fk+T0yJmNXP1wMfmc3lT506t7SqcCj190PjWZFP/5xyf49i1P84t7itPxb/63M/nXXz7AlQ+u4sX9VDv/j5cewZUPruq+/603ndC9Y8Ap8yby/rMP5vu3Leleq/2Bcw5hZnMdB09toCtX4Pf3r+TDvy1W2//yq4+hK1/gjqc3cNNj6/i7Y2dy7JxmPvH7v/HLe5ZVVGL/9LoWpoyvqbiZBuVWW1W8cPTXfmo+vPesg4c7HI0C/gRKkiSNUdl8gdd9504aajIcMLGeY+c0M7WxlvecOZ/VW9v5u2P637e9kpWmQB+f1BEoSacCF738KC44/UBOOKBvYj1lfA0ff/ECvnDN43zjDcfxoh7JfwiBf3vhYXzw3EP7LXBWnUnx8uNnUlud5vDp4zlk2ngA3nTKAcRId8G9qx9a3T1zohIUCpEr7l/JC/bjlohjRWmN/cbWLgAmN9Rw8rwJnHrQJKfea6+Y2EuSJI1BG1o6+fWiFQC0dOZ4dPU23nTKAQAcNKWBn73z1HKGN+TOO3I6v/2n0/nE7//Ga06cDUBTfRUnHjhxl+dc+Lz5vP6kA2iq639v9t1VLa/JpPm7Y3e+UBJCoOcpM5vruPPpDYN4F+UTY+wu9La3xQzHstJU/BWbi8terv3AGUwZX1POkFThTOwlSZLGmBgjz/nSzXTmCju1vz2pAD5WnHjgBK79wBmDOmdXSf3+MHFcFau2dvDrRctH7NZxf3hgJTOb68jlI396olgc8MLnHVTmqCpPaebI8k1tpAJMHFe9hzOk3TOxlyRJGmO2ted2Supfefwszl4wlflTRkaxvLHq6GQLtQ/95qERldhvbc/SVFfFwyu2dlf4f+vpcwG45p/P6J5WroGryRTX2OeSLRDTKfeo174xsZckSRpj1rcUK5m/+oTZnHvEVM4/qm+xOA2/lx0zg7uXbOSndy2jI5vvHtUtp3++/H6ufHAV0xtrWbOto7v9R3c8y8IDJ3QXC9Tg7G7ZhrQ3Uns+RJIkSaNJac/21y6cbVI/goQQuterl4qqlUuMkfO/fmv3TgBrtnUwvjbDz955CgdNHseh0xr47MuPKmuMle5D5x0GwLlHWHxQ+84Re0mSpFHiDw+sZFtHjn849cDdHveNG58CYN7kccMRlgahub641npzaxezmsu33eC2jhyPr9kOwK/ffRqPr97GC4+czrTGWm7+9zPLFtdo8uZTD2TRs5v45EuPKHcoGgVM7CVJkkaBjmy+e/3zhu2dvOOMeTTW9i30ls0XuH/5FgCmWoV7xGlOivNtbc+WNY61ybT7S954PCfNnchJc3e9W4D2TlNdFT9828nlDkOjhFPxJUmSKlRbV46lG1vpyOZ5/+X3d7d/46an+J+bF/c5/i+LN/DR3z5MvhD5zj+c6DrfEWhqYy0AX7nuibLGsSZZrjE9iUfSyOaIvSRJUgXK5Qs850s3s7kty2tPnM0Nj66lvjpNW1cegO/e+gwnHjiB846cDsBv7l3Bv//6we7zn3fIlLLErd2bN3kckxuqeWD5Fr7558UcM6uZ5x4yeadjYox05gpDWlyvVCjPxF6qDI7YS5IkVaDrH13L5rbidO1f37ui+P3dp/HfbzyeUw8qTpv+7q3PsHhdC9s7st1J/dGzmvjpO05xi7IR7NtvPhGAL//xCd78g79yw6Nrux8rFCJv+v5fWfAffySXL25Z+KtFy/nhX5YQYxzQ89+/bDOPrtq2U9vGlk6ue2QN//fgKn5x9zK+ev0TZFKBqY0u15AqgSP2kiRJFejepZsBmNVcx8ot7VzyxuM5cmYTR85s4mXHzuR9P7+Pqx5azQsuvoW6ZGT3P199NK8/6YByhq0BOCbZz77kC9c8xjkLppJKBb51y9Pc8fRGAM65+BZmT6jjL4uL90+ZN2m328/FGHntt+9k0dLNTG6o4f1nH8y9Szfz2OptrNnawfbOXPex42szXPz640bElnuS9szEXpIkqQLdv2wzJ82dwM/eeSrX/m01Lz5q+k6PHz6jkaseWg1AezbP5IZqXnvinHKEqkGqzuyYVPv3pxzAz/+6jFueWs9Zh03lziSpB1i6sY2lG9u677/zsns49aBJHHdAM887ZApzJtaTTgU2tHRy65Pr2dqeZVFyQWhDSyefuvIRAE48cALjajKcd+R0zl4wlb8u2cjZC6Yye0L9ML1jSfvKxF6SJKnCPL5mG/ct28K7zphHdSbFy4+b1eeY+VMadrr/kfMXkEpZLK9S/Pxdp3Djo+v48PmH8fO/LuNtP7yHH771JB5YvoU3n3oAx82ZwE2PrWXB9Ebe9ty5vPXSu7lv2RauuH8lV9y/EoD5U8bxzjMO4ge3L2HxuhYAqtKBOz92Dtl8gWUb29jWkeuzj/ph08cP+/uVtG9M7CVJkirIlrYuzv/6bQCcvWDaLo87YkZxSvZFrzhqj/vaa+Q5ff5kTp9fLJo3aVw1G1u7eNuP7gHguQdP4fyjpvOaE2d3H//LfzyNh1ZspbE2w61PbeDOpzdw42Pr+NgVDwNw0twJvO/sQ5jZVMvkhuK6+RlNdcP8riQNFRN7SZKkCnLp7UsA+Kcz53Pa/Em7PO6ASfU88Mlzaarru5e9Kstlbz+Zvy7ZxEVXPUoqwJmH9d3RoCqd4sQDJwBwyLTxvO30uTy1roX66jT3L9/Cc+ZPYlKDhfCk0crEXpIkaZit2NzGzY+vo1CIvPU58wZ17kMrt3LEjEY+cv6CPR7bXF+9tyFqBDlqVhNHzWri9PmTqEqHARW0S6VC95T6ORNdKy+Ndib2kiRJwyjGyHlfu5XWZL/5o2c38Zt7VzC5oYYPnnto93EhFNfDP7Z6G2/83l0E6N7e7u+OnTnscav8Dp+x64r3ksY2E3tJkqRh8LErHuaFR0yjM1egtStPdSZFV67Aq791Z/cx/33z4u7bf3fsTL7++uP4/f0r2ZIk9ABTxtfw3rMOHtbYJUkjm4m9JEnSEFu2sY3L717G5XcvA2B6Yy1//JczOPkLN9GVK/CmUw7g8ruXUYg7zrnywVU8vmYb2XxkzsQ6cvnIp152BOcfNaNM70KSNFKZ2EuSJA2x6x9ds9P9y95+Ms311XzrTSdw37LNfOi8Bbz7+fP528qtzJ5QzxEzG/m7/7mdR1ZtA+CD5x7KP59zSDlClyRVABN7SZKkIbRsYxtfu+HJ7vs/uGBhd1Gzcw6fxjmHF7esmzOxfqciZ//3vudy0MevAeANJ80ZxoglSZXGxF6SJGkI/PAvS/jM/z0KwPjaDH/56NlMrK+mrnrPFc2hWNX8PWfOZ9WWdqY21g5lqJKkCmdiL0mStB/l8gW+f/sSvnTt491tn3vFUcxqrhv0c314AFvaSZJkYi9JkrSfLN3Yymf+71FufnwdU8fX8NN3nsLkhhomjnM/eUnS0DGxlzRk2rvyLNvUxvwp48ikU+UOR5J2qytX4JeLlvPEmm3c9Ng6Nrd1cdi08WzvyPGu5x3EG06a0723/OJ1LVz90GqeWLuNprpq1mxt56l1LazY3A7AJ15yOG9/zjxSqVDOtyRJGiNCjHHPR40iCxcujIsWLSp3GNKY8Jpv3cGipZupyaT48dtP5pjZzf2uLV25pZ3pjbWk/QNY0j7KFyIrN7dz/aNr+PGdSzll3kQuesVR1FalyRcia7d1UJVO8fT6Fv7wwEqeWttCKhVoqqvirmc2sr0jRyrA2QumsqGliweWb6Emk6IzV2BCfRWTGmqIMfL0+lZSAWY01bG+pZMDJ9Zz6PTxHD2riRcfNYMDJtXvOVhJkgYphHBvjHFhn3YTe0n727rtHXz8ioe58bF1fR77x+cfxEfPX0CuEFm3vZPbnlzPR694GIDDpo3nqFlN1FenGV+bYVxNhnXbOjhw0jhef9IcxtU4yUgaSoVC5IEVW2isreLgqQ3lDoet7VlijBRisfjc0o2t5AqRTCpFvhBZsqGFGKE9m6e1M8em1iw/uWspG1o6+zzXwVMbWLaxja58obttXHWahtoMW9qyTG+q5bg5zbzsmJmctWBq94XGQiGyfHMbH/jFAzywfAsAJx44gRcdNZ2/O3YmUxtriTF2j+RLkjSUTOwTJvbS0NranuXYz1wPFEe8vvsPJ/Lqb9/Jg8u3MGV8Deu3F//gnjSumo2tXd3nNdZmCCGQzRfIpALbOnIA1FenaevKU5NJcei08cybPI7GugzTxteyoaWTe5dt5ogZjYyrydBcV82U8TVMaqjm+DnNfapID+SP740tndy9ZBP3Lt3Mwyu3UluV5oCJ9Rw0ZRyFuON5HlyxlUKMNNZWUShE6mvSjKvOUFedZkJ9NZl08XUyqcCkhhpOnjtxwJWwpeGSzRdYubmdlVvaeWz1Ni656anun710KnD0rCbWbutgQn01zfVVtHbl6czmqalK01ibobm+muPmNDOruZb2bJ7aTJoQoDNXIJNKkUkHtrR1saGli0Ihdv8Mjaspjp5n8wW6cgW2tmfZ3pGjM18gn4+s297B0+tb2dqeHfR7OnRaAy87ZibnHjmN2RPq+fIfH+cXdy+nqb6Ksw+byhEzG8kVIjOaann+oVMYV5OhUIgDmjLf2plj6cY2jpjZOOi4JEnaH0ZtYh9COB/4BpAGvh9j/NLujjexl4ZGV67Ask1tfPS3D7Fo6WZedNR0vvXmE4FiMakNLZ0cMaOJoz59HflC5Lg5zZx7xDSOm9PMaQdN6vNHdVeuQHs2T2Nthr8s3sgV96/gsdXbWb+9c6fRuBCKFwk6swW2d+a626vSgSNmNDJnYj3V6RSrt3Zw37LNNNRkmNpYy+wJdYyrTlNfk2Fbe5baqjQbWjq55cn1xFh83plNdTTXV/HshlZau/I7xddYm2FSQw3bO7KkQqC9K09rV647cemtOp2irjpNZ674PA01VUyor6KhNkNtJs3M5joOmdZAV67AARPrmTWhjoMmj2PZpjZWbekAihc5DpoyjrrqNI21VWxu6+L2pzbQlS9w8JQGGmoz5AuRrlyBrnyBdCheVJgyvobG5DFrHeydGCPfv20J6VTgjEMmM3fyuGJ/pgIbWrpYtqmNQow8s76VZ9a3sK0jS3u2QHNdFfXVaUIIychzJF+AeVPGUZ0OpEKgKp2iKl1MgqvTKbZ1ZNnSlqWlM0dr8hVCoCaTojqTojr5NyxEiCTJcjKqXYiRfIx0Zgt05vLF7/kCgWISD5BJpUinAl25Anct2ciWth3J87TGGv7p+fO55m9ruHfpZk48YALTmmpZvqmNdCowribTPS19a3uWtVs7WLOtY5/7t7E2w/jaKmoyxdgmNVQzf0oDM5vrqK1K05Ur0NaVY+6k4uc/V4gE6P75rqtOM64mTUNNhvrqvjN7cvkC6VRwVF2SVPFGZWIfQkgDTwLnAiuAe4A3xhgf3dU5JvYqp1Vb2nlg+RZecPg0qjMpYox05grUVlXGSG4p3vXbO3l6fXEK7IrNbTy7sY0r7lvB5rYsNZkULz56Bl981dH9vq/12zsZX5vZ6/dcKEQeX7Od255az0nzJnLs7ObuKbPZfIE1WztYvK6Fa/+2mifWbGdbR46VW4rFrF5y9Axqq9Ks3dbBis1ttHUVp+821lXRlStQV53mjIMnc/bh0zhs2nimNxVH/DuyeZ5Z37rTVlX1NWmqeiXJpf7Z3NZFZ7aYSBRiZOnGNm5fvIHtHVnG11YRY6SlM8fm1izbO7N0ZAs8vb5lpwRrf6tKB7L5SENNhulNtUwdX0MuHzlgUj1Hz2qiI5vnybUtpALMmlCXJJuB7R05tnfkyOYLdGQLdGTzbGnvIhAIAUII1FelySRJaioU994eX5Nhwrhq6pMkLJ8vJpz5QjG57Xl+AFIhuU/x/NKFnY5snlSSFLd25WnrytHSmWfZxlbqqjNkUoF0Kuz4ng5kUinqqtLUVqWY1ljLhHHV5PIFsvlIrlAgl48QoCqVIpUK5PKFYpKcxNeZK9DamSsm1l051mzt4NmNbeR3ddWml9qqFBPqq6nOpNjWnu2+KJQKxfeZSy6+DEQmSaZLn62ufIH+/tsOyXOX+q82k6Im6YOqdIoYixeXQqD471GIhABHz2riuQdPZtaEOqaOr+WAifXdv5sGciGoUIjct2wzddVp6qsztHbmSIVAdaY4VT5XKDCuOsOM5trk87Hj37IqXbyokTHhliRpwEZrYn8a8OkY43nJ/Y8BxBi/uKtzKiGx78zlue3JDfv0HKV/1Z7/vjvadm7p+RHofUzsdUzPT0vvz07vc3Zq63V+6dydnmFAr7mHY3bzfvt7zV3F1c/T7fI1ex6TLxSTn1ICs609y6bWLlZuaWf5prbuKa4AJxzQzOJ1LWzryDG+NsOE+mq6cgVCKI66FSKkw45kpZS8pELpfqo7mUmHQCr5+zuXL05vzRdKiVTxj+98jN3f84UdX6WEphAjuXzxe2/pVCBfiLRn8/2OSNdWpZg9oZ7zjpzG254zj8kNNX0PKqOObJ6qdGpEF+fLFyJb24sXRlZv7eCZ9S08ta6F+VPGMXtCPZl0YFt7jmc3ttKZzbOtI0dTXRVHzGyksbaKtds62N6R606WqjMpsvkCG1u6WLutg01tXdRXZdjc1sWarR2sb+kknQo8uHwLnUmSOS5Jwjt7JJ2pAOOqM1RlislyTVWK5roqoPgzUIjQ1pkjHyMxGTHO5SPbO7I7fd57Pl9p9DoZaN6l2qoUtVXp7mPGVaeTkdkMM5pqi5/tGJNEtZiwl6Z3d2SLFwZWb20nm9/xIqWfmQjdCX0mFUiliklxOlVMSsdVZ7rrPExuqObASeOYPaGOo2c1cfeSTbQnn6l8IVJfnWb+1AbSITBv8jhmNdftdlp3oRD526qt1FenSSc/uNl8oXtaeun3QWl0vGfSG5P3Gyj2Y6k/JUnS2DBaE/vXAOfHGN+Z3P8H4JQY4/t6HXchcCHAAQcccOLSpUuHPdbB2NjSyYmfu7HcYWgvpUJxqmsI0FhXRXNdFbMm1DFnQj2zJ9SxbFMbT61rIVAs5jR1fC2b27pYvbWdxtpiwlScMgqFAjuSliQRzxWKCfqO+8UkvvSjnEoVp/OmkwQmFQLpFD1u77gYUJqamk5BILlg0E+SkEumHNdVp6mtStNcX8Wh08YTgNkT6pk6vsYtnSrUto4sW9uyNNVXdX/+CoVItlAc4a5KB2oyeze7IpsvJGute16A6vs5iT0uCpSS/VKyva8KyQWp0gyE3kmwRc8kSVIl2VViPyZKTMcYvwt8F4oj9mUOZ48a66r4v/c9d5+fp7+/VUttxbGpHvd7HNvnsT7Pt+PgvseEXkfs+jX7i7P3+b3P7e81GcAxu3renc7r1Rc7v4fdx1W6X1wv67RSVY7G2h0JfUkqFahJpdnXTQhKa8f3JCTT8FPs/5+bVDKVfXevLUmSVOkqPbFfCczpcX920lbRqtIpjp7dVO4wJEmSJEkVoNLLI98DHBJCmBdCqAbeAFxZ5pgkSZIkSRo2FT1iH2PMhRDeB1xHcbu7S2OMj5Q5LEmSJEmShk1FJ/YAMcZrgGvKHYckSZIkSeVQ6VPxJUmSJEka00zsJUmSJEmqYCb2kiRJkiRVMBN7SZIkSZIqmIm9JEmSJEkVzMRekiRJkqQKZmIvSZIkSVIFM7GXJEmSJKmCmdhLkiRJklTBTOwlSZIkSapgIcZY7hiGVQhhPbC03HEM0GRgQ7mDGGPs8+Fnn5eH/T787PPhZ58PP/t8+Nnnw88+Lw/7vejAGOOU3o1jLrGvJCGERTHGheWOYyyxz4effV4e9vvws8+Hn30+/Ozz4WefDz/7vDzs991zKr4kSZIkSRXMxF6SJEmSpApmYj+yfbfcAYxB9vnws8/Lw34ffvb58LPPh599Pvzs8+Fnn5eH/b4brrGXJEmSJKmCOWIvSZIkSVIFM7GXJEmSJKmCmdhLkiRpwEIIodwxSJJ2ZmJfZv7nOPxCCOlyxzDWhBCaku/+zhkmIYTpyXd/xwyTEMKRIYTacscx1oQQnhNCmF/uOMaYunIHMNaU/nbxd/rwsc/Lx78X946dViYhhJNDCN8DPhJCmFLueMaCEMLCEMJPgE/6R+DQCyGkQgiNIYSrgEsAYoyFMoc16oUQjg8h3ARcBBCtkDrkQgjHhBBuBz4HTCp3PGNFCOGEEML1wM1AU7njGQtCCKeGEH4L/G8I4YVeKB96IYTTkr8X/zWEMN7f6UMvuVh4GfCJEMJE+3x4JLnRP4N/L+4tE/thFkJIhxC+SHG7hr8AJwCfCiFMK29ko1eSYP4P8B3gJmAG8OkQQn15Ixvdkl/K24EqYFYI4fXgVdihEoq+BvwYuCzG+K5yxzSGfAL4TYzxlTHGleAIz1AKIVSFEL5D8f/RS4DrgDOTx/z9MkRCCGcC3wSuAJ4A3gxMKGNIo14I4fnA/1C8eDUT+HgI4bzyRjW6hRAOovg5/xNwIHBRCOEl5Y1q9Ash/AvwO4oXU16UtHnhcJD8D3D4pYBlwOtijD8C/gU4Fae1DZkkwbwZOCfp8y8DEciVM64xYgGwAfg68KZktKFg0rP/JSMKDcD9McYfA4QQ5pvoDJ3kouFBQEuM8etJ27khhGbAKZxDpwa4BTgjxngV8Fvg8BBCxlGeIXU0cE+M8WfATyhetG0pb0ij3gnAX2KMl1OchTUNeENpqZWGxEnAY8nfi/8GPAC8NIQwp5xBjQHPAC8F/gn4GECMMe//oYPjH3zDIJm6dmhytwBcHmN8MoRQE2NcBawAJpcvwtGnV58TY7wixrglhHAusIjiqP0XQgiHly3IUaZnn/f4RbwY6AKWJF8XhBAOcFrb/tH7c07xj5BTQgj/EUL4C/AV4EchhBPLE+Ho07PPkyRyA3BGCOElIYTfA/9OcRT5Q8kxftb3g16f9dYY489jjO3J/QyQjzHmvJC1//Tz++U24LUhhE8C91H8f/SbIYTXliXAUaifPn8SaA4hzIgxbqZ4IaUaeEU54huNQggvCyG8L4RwatJ0DzAnhDAn6fO/AFuAV5UrxtGon36/Gngo+d5SmpJPcpFcA+N/gEMohNAcQrgauAF4XQihIcaYjzFuAYgxdoYQxgPzgFVlDHXU6KfPxyXtpURzM/D3McZzgVaKiabLIPZBf33eI5lZCGyLMT4CPAJ8CvhWMpXW3z97aVef8xjjNuB/gddQvOL9RmA18GpreeybPfT5DymOpl0aYzwP+D5wao8/WLSXdvX7JVl6UvodcgvwyhDCBEfs911/f7sAxBgfAM4H5gLviTGeSTHpOd+L5PtmV31OMbHfBlwWirUN5gD3U5yd5YygfRBCmBFC+D/gwxSXlPwwhHBejPEZ4E7gdcmhTwCPAhODxVH32W76PQ8UYowdwFeBd4QQJscYnV07CP5hPbTGUVz79/7k9hn9HHMK8EiMcVUIoSGEcMhwBjgK9e7z58GOUbMY46IY4zXJsdcCxwNtZYhzNOm3zxPLgPEhhF9S/CV+L/BkjDHrH+D7ZJd9HmO8BDgzxnhrjLET+D3FCyx+zvfN7j7nV1FMdkrrjRcBa4HOYYxvtNrl7/RkWU8KeDY55vnlCnKU2eXfLjHGu4EpFPscisvcxlO8UK69t6vP+VPAB4EvAr+OMb6S4kXyM5PHnRG09xYCt8UYz4gxXgR8AyjVprkNODqEcHKScK4EnpMkndo3vfv968C7YafP85+Buyj+PBBCOHn4w6xMJvb7WQjhLSGE54cQGpMiSt8FfgV0UJwiOzM5LpOc0gwsDyG8jeL0n+OGP+rKNtA+78eJFEczvRo4SIPo8wkU/whcQ/Eiyj8Bhzm6M3iD+Zwn0wdLTqS43Cc/rAGPAgPo81kAMcaHKE69f18IYTLFomJHARvLFHpFG8T/oyG5QFiTnNpRai9H3JVsEH1eA9wBvDc59RyKO0GY8AzSHvr85FKfxxi7Yox/ijH+Ijn1ROCP5Ym6siV9fmbyOb6JYq2Iko3AU8ntv1KcGfG1ZPbEkcCyYNHlvbKHft8EPJYcl4Li2nqKu8x8JISwFTjB3+sDE7zYt++SD9t04OcU19A/TfGK6wdijBuSY55DcVrPPTHGn/Y49yfAm4DLgK8lfyBqD/a2z0MIjRRnSXyBYrL5bzHGJ4f/HVSeQfb5ohjjT5K2yT0ebwCqY4ybyvAWKs4+fM5rgNOA/6J48crP+QDt7ec8af8gcBBwCPCvMcZHhzn8irUPn/V0UmDpp8DiGOOnyxF/JdqH3+lHUlxWNR3IAu+LMT42/O+g8uzj34vPpTiqvAH4xxjjs8MbfWXaU5+HEKpijNlQXNN9RIzx3T3OvRiYTbE6/ltijE8M/zuoTIPs98NjjP/U47z5FJe4dQH/EmN8uCxvogI5Yr+Pkj8qIsWpaCtjjOdQHJXcRPHqKwAxxr9QnLq2IBT39i6tn7qaYoX8t5nUD8xe9nlTCKE2WQ8bgc/FGF9msjMwe9HnhyV9Pi75BZ4OIaRijC0m9QOzD5/zumQKfhd+zgdlHz7n45P2iykm9OeZ1A/cPnzW65ORHYC3m9QP3F5+1puT3y+PABcAb40xnmNSPzD78Dkflzz0DPAfye+XZ4c1+Ao1wD4vLQs8F/hNct7UpO3DwDtijKeY1A/cXvT7b5PzJibnbQM+mfx+MakfhMyeD1F/QnFvxYuAdAjhGqCRZKprMnrwAWBVCOH5McZbktO+R3FqyU3AASGE43pMrdIe7GOf30ixz4+PMd5YhvAr0j72+Q3AgUmfWxxygPbj5/yOMoRfkfbn5zzGmC3DW6hI+7nfu8rwFirOfujzA0IIJ8Ti1PFnhv8dVJ799Dk/Mca4AgstD8hg+zyEUA2sB54MIXye4vZ2Z8bisrbtZXobFWc/9ftZMcZ1wLoyvY2K5oj9XgghPJ9iEbAJFLfzuojidLSzQlLgIRbX/X06+Sp5CfAeintiHh1jXD1sQVe4/djn/qc4QPuhzx/EPh8UP+fDz895edjvw28//n5ZOWxBV7j9+DlfMXxRV7ZB9vlnktNqgbdSHHgbD7wg7lyrRnuwH/vdWZ37wBH7vVMAvhp3rDc7nuKWdZ8EvgWcGIoFIH4PnB1CmJtMm+qg+KG9tSxRVzb7fPjZ58PPPh9+9nl52O/Dzz4ffvb58Btsn88GZgI/BS6OxW0dNXj2+wjgiP3euRf4VTLlBIr7uB4QY/wRxekn70+uSs0G8qW1UDHGP/hLeq/Z58PPPh9+9vnws8/Lw34ffvb58LPPh99g+rwQY1wRY7w7xvgWk8t9Yr+PACb2eyHG2BZj7Iw7ivacS3GNCMDbgMNDCFcBlwP3gdvv7Cv7fPjZ58PPPh9+9nl52O/Dzz4ffvb58Btkn98L9vn+YL+PDE7F3wfJVakITAOuTJq3Ax+nuIfxktJatBjdV3B/sM+Hn30+/Ozz4Wefl4f9Pvzs8+Fnnw8/+7w87PfycsR+3xSAKop7ih6TXIn6D4pTTG6PFpgZCvb58LPPh599Pvzs8/Kw34effT787PPhZ5+Xh/1eRsGLJfsmhHAqcEfy9cMY4w/KHNKoZ58PP/t8+Nnnw88+Lw/7ffjZ58PPPh9+9nl52O/lY2K/j5Kqjv9AsaJjZ7njGQvs8+Fnnw8/+3z42eflYb8PP/t8+Nnnw88+Lw/7vXxM7CVJkiRJqmCusZckSZIkqYKZ2EuSJEmSVMFM7CVJkiRJqmAm9pIkSZIkVTATe0mSJEmSKpiJvSRJ6iMU3R5CeFGPtteGEP5YzrgkSVJfbncnSZL6FUI4Cvg1cDyQAe4Hzo8xPr0Xz5WJMeb2c4iSJAkTe0mStBshhC8DrcC45PuBwFFAFfDpGOMfQghzgZ8kxwC8L8Z4RwjhTOAiYDOwgOIFgl8Bs4E0cFGM8ZfD9mYkSRqlTOwlSdIuhRDGAfcBXcBVwCMxxp+GEJqBuykm6xEoxBg7QgiHAJfHGBcmif3VwFExxiUhhFdTHPF/V/LcTTHGrcP+piRJGmVM7CVJ0m6FED4LtACvA2qB0pT6icB5wCrgf4DjgDxwaIyxPknsPxVjPCt5nkOB64FfAlfFGG8bvnchSdLolSl3AJIkacQrJF8BeHWM8YmeD4YQPg2sBY6lWJi3o8fDraUbMcYnQwgnAC8GPhdCuCnG+Nkhjl2SpFHPqviSJGmgrgPeH0IIACGE45P2JmB1jLEA/APF9fN9hBBmAm0xxp8CXwFOGPqQJUka/RyxlyRJA3UR8HXgoRBCClgCvBT4JvDbEMJbgD/SY5S+l6OBr4QQCkAW+Kchj1iSpDHANfaSJEmSJFUwp+JLkiRJklTBTOwlSZIkSapgJvaSJEmSJFUwE3tJkiRJkiqYib0kSZIkSRXMxF6SJEmSpApmYi9JkiRJUgX7/3mM5w7krmPwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graph bitcoin price over the years\n",
    "df.Weighted_Price.plot(title = \"Bitcoin Price\", figsize=(14,6))\n",
    "plt.tight_layout()\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('US Dollars')\n",
    "plt.show()\n",
    "# As the graph shows 2017-2021 price behavior looks signficantly different than 2012-2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "70ce3c82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:38:57.018807Z",
     "iopub.status.busy": "2021-07-30T22:38:57.017428Z",
     "iopub.status.idle": "2021-07-30T22:38:57.349281Z",
     "shell.execute_reply": "2021-07-30T22:38:57.348680Z",
     "shell.execute_reply.started": "2021-07-30T02:30:17.997536Z"
    },
    "id": "gXWGE_8w6MJC",
    "outputId": "e2d91537-2dd5-4d69-dee7-bc97d4a9759d",
    "papermill": {
     "duration": 0.405045,
     "end_time": "2021-07-30T22:38:57.349453",
     "exception": false,
     "start_time": "2021-07-30T22:38:56.944408",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the autocorrelation to see if there are a large number of lags in the time series.\n",
    "autocorrelation_plot(df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0bc1c2bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:38:57.478932Z",
     "iopub.status.busy": "2021-07-30T22:38:57.478164Z",
     "iopub.status.idle": "2021-07-30T22:38:57.804462Z",
     "shell.execute_reply": "2021-07-30T22:38:57.803763Z",
     "shell.execute_reply.started": "2021-07-30T02:30:21.150242Z"
    },
    "id": "GidUoXSxuDd-",
    "outputId": "b09e5d5c-3689-44b7-f89f-43a32c594bd8",
    "papermill": {
     "duration": 0.407688,
     "end_time": "2021-07-30T22:38:57.804648",
     "exception": false,
     "start_time": "2021-07-30T22:38:57.396960",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's look at the past 200 days to possibly adjust our data to this period\n",
    "df.Weighted_Price.iloc[-200:].plot(title = \"Bitcoin Price\", figsize=(14,6))\n",
    "plt.tight_layout()\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('US Dollars')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a82ff355",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:38:57.908810Z",
     "iopub.status.busy": "2021-07-30T22:38:57.907696Z",
     "iopub.status.idle": "2021-07-30T22:39:03.189443Z",
     "shell.execute_reply": "2021-07-30T22:39:03.190246Z",
     "shell.execute_reply.started": "2021-07-30T02:32:17.774244Z"
    },
    "id": "R4Bjd0X_0FNT",
    "outputId": "0ee58906-0191-4dcf-91e2-3861793eb8c0",
    "papermill": {
     "duration": 5.336756,
     "end_time": "2021-07-30T22:39:03.190524",
     "exception": false,
     "start_time": "2021-07-30T22:38:57.853768",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 7)\n"
     ]
    }
   ],
   "source": [
    "# Since the first couple years of bitcoin don't properly represent the movement and volatility of the price -\n",
    "# I decide to simply focus on the previous 4 years of data from March 31st, 2021.\n",
    "df2 = pd.read_csv('/kaggle/input/bitcoin-historical-data/bitstampUSD_1-min_data_2012-01-01_to_2021-03-31.csv')\n",
    "df2.Timestamp = pd.to_datetime(df2.Timestamp, unit='s')\n",
    "df2.index = df2.Timestamp\n",
    "df2 = df2.resample('D').mean()\n",
    "df2 = df2.dropna()\n",
    "# changes data to strictly to the previous 4 years, which is March 2017 to March 2021\n",
    "df2 = df2.iloc[(-365*4):]\n",
    "print(df2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "761d2b3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:39:03.299947Z",
     "iopub.status.busy": "2021-07-30T22:39:03.298902Z",
     "iopub.status.idle": "2021-07-30T22:39:03.847855Z",
     "shell.execute_reply": "2021-07-30T22:39:03.847226Z",
     "shell.execute_reply.started": "2021-07-30T02:32:35.190754Z"
    },
    "id": "skDQMwjrCQxg",
    "outputId": "9dba56fa-eec7-47dd-9020-77acfa3d3ed7",
    "papermill": {
     "duration": 0.611387,
     "end_time": "2021-07-30T22:39:03.848032",
     "exception": false,
     "start_time": "2021-07-30T22:39:03.236645",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.Weighted_Price.plot(title = \"Bitcoin Price\", figsize=(14,6))\n",
    "plt.tight_layout()\n",
    "plt.xlabel('Dates')\n",
    "plt.ylabel('$ Price')\n",
    "plt.show()\n",
    "# This data looks much more relevant for training a model. \n",
    "# However the recent spike will be involved in the testing data split - a tough prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e77f0a7",
   "metadata": {
    "id": "UWckWTiDxv7u",
    "papermill": {
     "duration": 0.071105,
     "end_time": "2021-07-30T22:39:03.984317",
     "exception": false,
     "start_time": "2021-07-30T22:39:03.913212",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **1A. ROLLING FORECAST ARIMA**\n",
    "I first created a rolling forecast ARIMA model in order to get a baseline for what a great RMSE is. However, since this model predicts in rolling day by day increments, it isn’t fair to compare to the other models. As plotted below, the predictions are in red and the actual prices are in blue. However, we have to zoom in more to distinguish the two.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "41760f05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:39:04.101317Z",
     "iopub.status.busy": "2021-07-30T22:39:04.100125Z",
     "iopub.status.idle": "2021-07-30T22:39:04.409851Z",
     "shell.execute_reply": "2021-07-30T22:39:04.411153Z",
     "shell.execute_reply.started": "2021-07-30T02:32:51.153821Z"
    },
    "id": "_DFXi8WZA2oA",
    "papermill": {
     "duration": 0.37148,
     "end_time": "2021-07-30T22:39:04.411467",
     "exception": false,
     "start_time": "2021-07-30T22:39:04.039987",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "# We're going to create a dataframe for just the price (the index is still the date)\n",
    "price = df2.Weighted_Price\n",
    "# Next we're going to assign 70% percent of the data to training and 30% for testing\n",
    "X = price.values\n",
    "size = int(len(X) * 0.7)\n",
    "train, test = X[0:size], X[size:len(X)]\n",
    "history = [x for x in train]\n",
    "predictions = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b15cd7a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:39:04.526132Z",
     "iopub.status.busy": "2021-07-30T22:39:04.525085Z",
     "iopub.status.idle": "2021-07-30T22:41:01.498783Z",
     "shell.execute_reply": "2021-07-30T22:41:01.499282Z",
     "shell.execute_reply.started": "2021-07-30T02:32:54.042505Z"
    },
    "id": "Qme9vi6pBzJV",
    "outputId": "479d30b6-c996-4425-bf0e-fafd131ee035",
    "papermill": {
     "duration": 117.036332,
     "end_time": "2021-07-30T22:41:01.499500",
     "exception": false,
     "start_time": "2021-07-30T22:39:04.463168",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "predicted=10258.930907, expected=10143.891251\n",
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      "predicted=10154.275954, expected=9582.246888\n",
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      "predicted=8966.647588, expected=8796.161047\n",
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      "predicted=8543.884092, expected=8747.601872\n",
      "predicted=8794.765866, expected=8791.293455\n",
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      "predicted=6796.451294, expected=6914.666410\n",
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      "predicted=13072.153152, expected=13067.709654\n",
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      "predicted=17025.992318, expected=16987.868828\n",
      "predicted=16969.826458, expected=17312.290465\n",
      "predicted=17429.734041, expected=17991.419820\n",
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      "predicted=19087.429744, expected=18998.079583\n",
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      "predicted=18348.230150, expected=17937.337421\n",
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      "predicted=23206.633842, expected=22884.613915\n",
      "predicted=22875.581617, expected=23360.905627\n",
      "predicted=23459.796787, expected=23594.411731\n",
      "predicted=23762.453927, expected=23259.466834\n",
      "predicted=23143.853048, expected=23097.830404\n",
      "predicted=23091.129937, expected=23530.788342\n",
      "predicted=23655.083882, expected=23219.253678\n",
      "predicted=23132.771174, expected=24016.394662\n",
      "predicted=24211.458419, expected=25267.112615\n",
      "predicted=25579.453361, expected=27043.386470\n",
      "predicted=27436.659294, expected=26964.020499\n",
      "predicted=26941.227497, expected=26671.008099\n",
      "predicted=26612.905876, expected=28141.234408\n",
      "predicted=28590.126289, expected=28881.524878\n",
      "predicted=28998.793334, expected=29288.824917\n",
      "predicted=29359.664403, expected=30757.429533\n",
      "predicted=31209.557434, expected=33467.362526\n",
      "predicted=34201.199560, expected=31856.122685\n",
      "predicted=31407.508267, expected=32273.087909\n",
      "predicted=32552.099720, expected=34904.997383\n",
      "predicted=35559.775015, expected=38067.445472\n",
      "predicted=38519.394580, expected=39807.603739\n",
      "predicted=40326.274078, expected=40395.370088\n",
      "predicted=40888.417879, expected=39727.277526\n",
      "predicted=39831.050419, expected=34277.110008\n",
      "predicted=32355.580394, expected=34840.250153\n",
      "predicted=35247.394182, expected=34673.974752\n",
      "predicted=34661.775068, expected=38508.456683\n",
      "predicted=39169.554267, expected=37468.427964\n",
      "predicted=37601.688625, expected=36843.154360\n",
      "predicted=36160.724557, expected=35650.764186\n",
      "predicted=35893.995378, expected=36046.172744\n",
      "predicted=36082.516063, expected=36858.910679\n",
      "predicted=37195.994035, expected=35152.737304\n",
      "predicted=34655.497297, expected=32949.072452\n",
      "predicted=32387.072393, expected=31861.057341\n",
      "predicted=31966.729956, expected=32351.814827\n",
      "predicted=32668.151751, expected=32225.070120\n",
      "predicted=32141.073592, expected=33353.085421\n",
      "predicted=33453.228883, expected=31997.221464\n",
      "predicted=31770.581892, expected=31149.419364\n",
      "predicted=30797.893964, expected=31793.884442\n",
      "predicted=32259.166773, expected=35051.467814\n",
      "predicted=35830.847851, expected=34053.712617\n",
      "predicted=33594.085161, expected=33450.937859\n",
      "predicted=32906.205464, expected=33705.975191\n",
      "predicted=34330.445034, expected=34713.696400\n",
      "predicted=34910.544458, expected=36573.094810\n",
      "predicted=36843.119221, expected=37541.548132\n",
      "predicted=37598.278048, expected=37630.049214\n",
      "predicted=37481.027877, expected=39814.356555\n",
      "predicted=40426.928339, expected=38711.774555\n",
      "predicted=38609.175075, expected=41196.614445\n",
      "predicted=41388.439324, expected=46684.490850\n",
      "predicted=48319.664312, expected=45798.733715\n",
      "predicted=45288.568274, expected=46272.215669\n",
      "predicted=46083.050143, expected=47603.415420\n",
      "predicted=48491.648908, expected=47273.849919\n",
      "predicted=47441.842964, expected=48600.491518\n",
      "predicted=48783.097013, expected=47862.397946\n",
      "predicted=47833.005241, expected=48845.166003\n",
      "predicted=49099.032022, expected=50913.924139\n",
      "predicted=51344.122999, expected=51893.311181\n",
      "predicted=52104.152287, expected=53083.879109\n",
      "predicted=53219.353739, expected=56271.288538\n",
      "predicted=57093.541190, expected=57040.825944\n",
      "predicted=57384.523994, expected=54708.312025\n",
      "predicted=54305.297988, expected=48968.837946\n",
      "predicted=47876.158353, expected=49800.284869\n",
      "predicted=50727.137310, expected=49985.234815\n",
      "predicted=50281.563344, expected=46655.920049\n",
      "predicted=45457.871966, expected=47130.208060\n",
      "predicted=47305.634073, expected=44818.447552\n",
      "predicted=44446.198495, expected=47641.536899\n",
      "predicted=48082.161072, expected=48617.289321\n",
      "predicted=48507.176744, expected=50429.449016\n",
      "predicted=50568.632024, expected=49398.250399\n",
      "predicted=49155.283760, expected=47717.643300\n",
      "predicted=47628.426617, expected=48371.543538\n",
      "predicted=48750.950127, expected=50218.216804\n",
      "predicted=50737.725514, expected=50798.354212\n",
      "predicted=50670.469632, expected=53980.847262\n",
      "predicted=54355.034762, expected=55375.366241\n",
      "predicted=55675.700872, expected=56272.596972\n",
      "predicted=56453.619933, expected=56826.560033\n",
      "predicted=57039.187797, expected=58802.601568\n",
      "predicted=59462.397263, expected=60455.844831\n",
      "predicted=60915.676841, expected=57507.734509\n",
      "predicted=56971.059041, expected=55339.804792\n",
      "predicted=55098.508262, expected=56158.017320\n",
      "predicted=56765.267571, expected=58472.117322\n",
      "predicted=58981.780987, expected=58283.547994\n",
      "predicted=57784.295493, expected=58779.769387\n",
      "predicted=58711.469042, expected=57312.598536\n",
      "predicted=57241.128143, expected=56787.148422\n",
      "predicted=56908.807902, expected=54703.387740\n",
      "predicted=54454.994708, expected=55123.993855\n",
      "predicted=55283.608974, expected=52121.684110\n",
      "predicted=51603.782980, expected=53241.037489\n",
      "predicted=53305.217834, expected=55193.357260\n",
      "predicted=55662.116754, expected=55832.958824\n",
      "predicted=55685.021642, expected=56913.993819\n",
      "predicted=56907.895725, expected=58346.912268\n",
      "predicted=58730.376166, expected=58764.349363\n",
      "Test RMSE: 914.737\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# walk-forward validation\n",
    "for t in range(len(test)):\n",
    "\tmodel = ARIMA(history, order=(5,1,0))\n",
    "\tmodel_fit = model.fit()\n",
    "\toutput = model_fit.forecast()\n",
    "\tyhat = output[0]\n",
    "\tpredictions.append(yhat)\n",
    "\tobs = test[t]\n",
    "\thistory.append(obs)\n",
    "\tprint('predicted=%f, expected=%f' % (yhat, obs))\n",
    "# evaluate forecasts\n",
    "rmse = sqrt(mean_squared_error(test, predictions))\n",
    "print('Test RMSE: %.3f' % rmse)\n",
    "# plot forecasts against actual outcomes\n",
    "plt.plot(test)\n",
    "plt.plot(predictions, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6c6b0ffc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:01.788492Z",
     "iopub.status.busy": "2021-07-30T22:41:01.787791Z",
     "iopub.status.idle": "2021-07-30T22:41:01.976127Z",
     "shell.execute_reply": "2021-07-30T22:41:01.975467Z",
     "shell.execute_reply.started": "2021-07-30T02:41:07.302637Z"
    },
    "id": "WEBwDpuHM5ZQ",
    "outputId": "9aa61012-243b-405f-ea5c-1fcd86a2aa17",
    "papermill": {
     "duration": 0.336577,
     "end_time": "2021-07-30T22:41:01.976312",
     "exception": false,
     "start_time": "2021-07-30T22:41:01.639735",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 914.737\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# if we look at this model, the predicted is indistinuishable from the actual price\n",
    "# this is simply because it's predicting day by day.\n",
    "plt.figure(figsize=(15,8))\n",
    "rmse = sqrt(mean_squared_error(test, predictions))\n",
    "print('Test RMSE: %.3f' % rmse)\n",
    "# plot forecasts against actual outcomes\n",
    "plt.plot(test)\n",
    "plt.plot(predictions, color='red')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('$ Price')\n",
    "plt.title('Predicted vs. Actual BTC Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a57de86b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:02.296260Z",
     "iopub.status.busy": "2021-07-30T22:41:02.295533Z",
     "iopub.status.idle": "2021-07-30T22:41:02.507927Z",
     "shell.execute_reply": "2021-07-30T22:41:02.507314Z",
     "shell.execute_reply.started": "2021-07-30T02:41:11.582398Z"
    },
    "id": "ivTOPpR-RVUy",
    "outputId": "28b7cbc8-1701-4317-d907-b2efed61db67",
    "papermill": {
     "duration": 0.373562,
     "end_time": "2021-07-30T22:41:02.508085",
     "exception": false,
     "start_time": "2021-07-30T22:41:02.134523",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 914.737\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I plot 50 days to more accurately see how the models works with its lag\n",
    "plt.figure(figsize=(15,8))\n",
    "rmse = sqrt(mean_squared_error(test, predictions))\n",
    "print('Test RMSE: %.3f' % rmse)\n",
    "# plot forecasts against actual outcomes\n",
    "plt.plot(test[-50:])\n",
    "plt.plot(predictions[-50:], color='red')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('$ Price')\n",
    "plt.title('Predicted vs. Expected BTC Price Forecast')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4a7b3e",
   "metadata": {
    "id": "O3_swfzuxoqg",
    "papermill": {
     "duration": 0.145557,
     "end_time": "2021-07-30T22:41:02.801818",
     "exception": false,
     "start_time": "2021-07-30T22:41:02.656261",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **2. PROPHET**\n",
    "The Prophet library is an open-source library developed by Facebook and designed for automatic forecasting of time series data. This model mainly focuses on predicting trends and seasonality. However due to its easy use I decided to implement this model first with default settings. As plotted below the prophet model predicts the price of bitcoin will fall. In addition, the model provides upper and lower bounds estimates as well (edges of shaded region). While the upper bounds estimate has an accurate slope direction, this model didn’t perform well when compared to the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "800cc98f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:03.103201Z",
     "iopub.status.busy": "2021-07-30T22:41:03.102464Z",
     "iopub.status.idle": "2021-07-30T22:41:03.323130Z",
     "shell.execute_reply": "2021-07-30T22:41:03.322043Z",
     "shell.execute_reply.started": "2021-07-30T02:41:15.281375Z"
    },
    "id": "2TUVRzxWKhhO",
    "outputId": "487389e3-582b-4ff0-ba3e-86466450e9d6",
    "papermill": {
     "duration": 0.373673,
     "end_time": "2021-07-30T22:41:03.323374",
     "exception": false,
     "start_time": "2021-07-30T22:41:02.949701",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prophet 0.7.1\n"
     ]
    }
   ],
   "source": [
    "# importing Prophet model\n",
    "# importing prophet library\n",
    "import fbprophet\n",
    "from fbprophet import Prophet\n",
    "# print version number\n",
    "print('Prophet %s' % fbprophet.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0b4701d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:03.659510Z",
     "iopub.status.busy": "2021-07-30T22:41:03.658439Z",
     "iopub.status.idle": "2021-07-30T22:41:03.661916Z",
     "shell.execute_reply": "2021-07-30T22:41:03.661369Z",
     "shell.execute_reply.started": "2021-07-30T02:41:20.503545Z"
    },
    "id": "bygQZ8FhexNS",
    "papermill": {
     "duration": 0.167813,
     "end_time": "2021-07-30T22:41:03.662076",
     "exception": false,
     "start_time": "2021-07-30T22:41:03.494263",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# For prophet we have to fit the dataframe to two columns: ds, y\n",
    "df3 = df2.copy()\n",
    "df3.reset_index(inplace=True)\n",
    "df3 = df3[['Timestamp', 'Weighted_Price']]\n",
    "df3.columns = ['ds', 'y']\n",
    "# Next the data is split as usual\n",
    "X = df3\n",
    "size = int(len(X) * 0.7)\n",
    "train, test = X[0:size], X[size:len(X)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "09839863",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:03.959597Z",
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     "shell.execute_reply": "2021-07-30T22:41:05.964804Z",
     "shell.execute_reply.started": "2021-07-30T02:41:23.119705Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7f322c64e750>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Prophet()\n",
    "# fit the model\n",
    "model.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b9a6957f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:06.291703Z",
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     "shell.execute_reply.started": "2021-07-30T02:41:28.276654Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 25141.556\n"
     ]
    }
   ],
   "source": [
    "# frequency is a day and periods are length of test data\n",
    "future = model.make_future_dataframe(periods=len(test), freq='D')\n",
    "forecasting = model.predict(future)\n",
    "# prediction is stored and RMSE is returned\n",
    "y_true = test['y'].values\n",
    "y_pred = forecasting.yhat.values[-len(test):]\n",
    "rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n",
    "print('Test RMSE: %.3f' % rmse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "08d79410",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-07-30T02:41:36.710593Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>trend</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>trend_lower</th>\n",
       "      <th>trend_upper</th>\n",
       "      <th>additive_terms</th>\n",
       "      <th>additive_terms_lower</th>\n",
       "      <th>additive_terms_upper</th>\n",
       "      <th>weekly</th>\n",
       "      <th>weekly_lower</th>\n",
       "      <th>weekly_upper</th>\n",
       "      <th>yearly</th>\n",
       "      <th>yearly_lower</th>\n",
       "      <th>yearly_upper</th>\n",
       "      <th>multiplicative_terms</th>\n",
       "      <th>multiplicative_terms_lower</th>\n",
       "      <th>multiplicative_terms_upper</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-04-02</td>\n",
       "      <td>3038.838847</td>\n",
       "      <td>-550.444742</td>\n",
       "      <td>2022.184105</td>\n",
       "      <td>3038.838847</td>\n",
       "      <td>3038.838847</td>\n",
       "      <td>-2351.980901</td>\n",
       "      <td>-2351.980901</td>\n",
       "      <td>-2351.980901</td>\n",
       "      <td>-2.123923</td>\n",
       "      <td>-2.123923</td>\n",
       "      <td>-2.123923</td>\n",
       "      <td>-2349.856978</td>\n",
       "      <td>-2349.856978</td>\n",
       "      <td>-2349.856978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>686.857946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-04-03</td>\n",
       "      <td>3034.869401</td>\n",
       "      <td>-454.637607</td>\n",
       "      <td>2033.147564</td>\n",
       "      <td>3034.869401</td>\n",
       "      <td>3034.869401</td>\n",
       "      <td>-2287.256094</td>\n",
       "      <td>-2287.256094</td>\n",
       "      <td>-2287.256094</td>\n",
       "      <td>19.057330</td>\n",
       "      <td>19.057330</td>\n",
       "      <td>19.057330</td>\n",
       "      <td>-2306.313424</td>\n",
       "      <td>-2306.313424</td>\n",
       "      <td>-2306.313424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>747.613307</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-04-04</td>\n",
       "      <td>3030.899955</td>\n",
       "      <td>-453.198266</td>\n",
       "      <td>1988.199030</td>\n",
       "      <td>3030.899955</td>\n",
       "      <td>3030.899955</td>\n",
       "      <td>-2245.027779</td>\n",
       "      <td>-2245.027779</td>\n",
       "      <td>-2245.027779</td>\n",
       "      <td>16.791104</td>\n",
       "      <td>16.791104</td>\n",
       "      <td>16.791104</td>\n",
       "      <td>-2261.818883</td>\n",
       "      <td>-2261.818883</td>\n",
       "      <td>-2261.818883</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>785.872176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>3026.930508</td>\n",
       "      <td>-419.134369</td>\n",
       "      <td>2022.207908</td>\n",
       "      <td>3026.930508</td>\n",
       "      <td>3026.930508</td>\n",
       "      <td>-2215.796077</td>\n",
       "      <td>-2215.796077</td>\n",
       "      <td>-2215.796077</td>\n",
       "      <td>1.484618</td>\n",
       "      <td>1.484618</td>\n",
       "      <td>1.484618</td>\n",
       "      <td>-2217.280695</td>\n",
       "      <td>-2217.280695</td>\n",
       "      <td>-2217.280695</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>811.134432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>3022.961062</td>\n",
       "      <td>-401.753789</td>\n",
       "      <td>2054.310265</td>\n",
       "      <td>3022.961062</td>\n",
       "      <td>3022.961062</td>\n",
       "      <td>-2198.650481</td>\n",
       "      <td>-2198.650481</td>\n",
       "      <td>-2198.650481</td>\n",
       "      <td>-25.154687</td>\n",
       "      <td>-25.154687</td>\n",
       "      <td>-25.154687</td>\n",
       "      <td>-2173.495794</td>\n",
       "      <td>-2173.495794</td>\n",
       "      <td>-2173.495794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>824.310581</td>\n",
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      "text/plain": [
       "          ds        trend  yhat_lower   yhat_upper  trend_lower  trend_upper  \\\n",
       "0 2017-04-02  3038.838847 -550.444742  2022.184105  3038.838847  3038.838847   \n",
       "1 2017-04-03  3034.869401 -454.637607  2033.147564  3034.869401  3034.869401   \n",
       "2 2017-04-04  3030.899955 -453.198266  1988.199030  3030.899955  3030.899955   \n",
       "3 2017-04-05  3026.930508 -419.134369  2022.207908  3026.930508  3026.930508   \n",
       "4 2017-04-06  3022.961062 -401.753789  2054.310265  3022.961062  3022.961062   \n",
       "\n",
       "   additive_terms  additive_terms_lower  additive_terms_upper     weekly  \\\n",
       "0    -2351.980901          -2351.980901          -2351.980901  -2.123923   \n",
       "1    -2287.256094          -2287.256094          -2287.256094  19.057330   \n",
       "2    -2245.027779          -2245.027779          -2245.027779  16.791104   \n",
       "3    -2215.796077          -2215.796077          -2215.796077   1.484618   \n",
       "4    -2198.650481          -2198.650481          -2198.650481 -25.154687   \n",
       "\n",
       "   weekly_lower  weekly_upper       yearly  yearly_lower  yearly_upper  \\\n",
       "0     -2.123923     -2.123923 -2349.856978  -2349.856978  -2349.856978   \n",
       "1     19.057330     19.057330 -2306.313424  -2306.313424  -2306.313424   \n",
       "2     16.791104     16.791104 -2261.818883  -2261.818883  -2261.818883   \n",
       "3      1.484618      1.484618 -2217.280695  -2217.280695  -2217.280695   \n",
       "4    -25.154687    -25.154687 -2173.495794  -2173.495794  -2173.495794   \n",
       "\n",
       "   multiplicative_terms  multiplicative_terms_lower  \\\n",
       "0                   0.0                         0.0   \n",
       "1                   0.0                         0.0   \n",
       "2                   0.0                         0.0   \n",
       "3                   0.0                         0.0   \n",
       "4                   0.0                         0.0   \n",
       "\n",
       "   multiplicative_terms_upper        yhat  \n",
       "0                         0.0  686.857946  \n",
       "1                         0.0  747.613307  \n",
       "2                         0.0  785.872176  \n",
       "3                         0.0  811.134432  \n",
       "4                         0.0  824.310581  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecasting.head()"
   ]
  },
  {
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    "tags": []
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   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting forecast\n",
    "model.plot(forecasting)\n",
    "plt.title('Prophet Forecast for BTC Price')\n",
    "plt.ylabel('$ Price')\n",
    "plt.xlabel('Date')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8a365892",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:13.156080Z",
     "iopub.status.busy": "2021-07-30T22:41:13.155088Z",
     "iopub.status.idle": "2021-07-30T22:41:13.666346Z",
     "shell.execute_reply": "2021-07-30T22:41:13.666907Z",
     "shell.execute_reply.started": "2021-07-30T02:41:42.707271Z"
    },
    "id": "UDHU5yunniLd",
    "outputId": "415217b8-d5f2-4ca1-d03d-c71851c6ec34",
    "papermill": {
     "duration": 0.688163,
     "end_time": "2021-07-30T22:41:13.667086",
     "exception": false,
     "start_time": "2021-07-30T22:41:12.978923",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting forecast with test data\n",
    "ax = forecasting.plot(x='ds', y='yhat', legend=True, label=\"Predicted\", figsize=(15,8), color='red')\n",
    "forecasting.plot(x='ds', y='yhat_upper', legend=True, label='Upper Prediction', ax=ax, color='yellow')\n",
    "forecasting.plot(x='ds', y='yhat_lower', legend=True, label='Lower Prediction', ax=ax, color='purple')\n",
    "test.plot(x='ds', y='y', legend=True, label='Actual', ax=ax)\n",
    "plt.ylabel('$ Price')\n",
    "plt.xlabel('Date')\n",
    "plt.title('Predicted and Expected BTC Price (Prophet)')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "389f1e5e",
   "metadata": {
    "id": "G9GsgQoA2XBL",
    "papermill": {
     "duration": 0.161745,
     "end_time": "2021-07-30T22:41:13.988626",
     "exception": false,
     "start_time": "2021-07-30T22:41:13.826881",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **3. ARIMA MODEL**\n",
    "Next is ARIMA, an acronym that stands for AutoRegressive Integrated Moving Average. ARIMA is a widely used statistical method for analyzing and forecasting time series data. It consists of a suite of standard structures in time series data and provides a simple yet powerful method for making skillful time series forecasts. ARIMA models have 3 parameters (p, d, q), which indicate the specific ARIMA model being used. I simply applied parameters that I’ve used on different time series data in the past, which could’ve possibly led to an unfavorable model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "577e17c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:14.313620Z",
     "iopub.status.busy": "2021-07-30T22:41:14.312578Z",
     "iopub.status.idle": "2021-07-30T22:41:14.315034Z",
     "shell.execute_reply": "2021-07-30T22:41:14.315534Z",
     "shell.execute_reply.started": "2021-07-30T02:41:46.583955Z"
    },
    "id": "GCgo032zc69X",
    "papermill": {
     "duration": 0.167257,
     "end_time": "2021-07-30T22:41:14.315711",
     "exception": false,
     "start_time": "2021-07-30T22:41:14.148454",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "# method to be used later\n",
    "def difference(dataset, interval=1):\n",
    "    diff = list()\n",
    "    for i in range(interval, len(dataset)):\n",
    "        value = dataset[i] - dataset[i-interval]\n",
    "        diff.append(value)\n",
    "    return np.array(diff)\n",
    "\n",
    "def inverse_difference(history, yhat, interval=1):\n",
    "    return yhat + history[-interval]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8ffb769a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:14.627010Z",
     "iopub.status.busy": "2021-07-30T22:41:14.624717Z",
     "iopub.status.idle": "2021-07-30T22:41:14.631241Z",
     "shell.execute_reply": "2021-07-30T22:41:14.632171Z",
     "shell.execute_reply.started": "2021-07-30T02:41:51.405592Z"
    },
    "id": "n0Uf_gnG1rzd",
    "outputId": "0d46f2b5-3f00-4442-d967-94c56b5b8f25",
    "papermill": {
     "duration": 0.162963,
     "end_time": "2021-07-30T22:41:14.632521",
     "exception": false,
     "start_time": "2021-07-30T22:41:14.469558",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "439\n"
     ]
    }
   ],
   "source": [
    "print(len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4ae7a001",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:14.964516Z",
     "iopub.status.busy": "2021-07-30T22:41:14.963415Z",
     "iopub.status.idle": "2021-07-30T22:41:15.393816Z",
     "shell.execute_reply": "2021-07-30T22:41:15.392689Z",
     "shell.execute_reply.started": "2021-07-30T02:41:53.624062Z"
    },
    "id": "4APW3q1Q1D86",
    "outputId": "9ded2f98-526f-4a1c-edb8-73b50db7abca",
    "papermill": {
     "duration": 0.603311,
     "end_time": "2021-07-30T22:41:15.394115",
     "exception": false,
     "start_time": "2021-07-30T22:41:14.790804",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Day 1: 3678.996300\n",
      "Predicted Day 2: 3690.584941\n",
      "Predicted Day 3: 3595.145596\n",
      "Predicted Day 4: 3545.495392\n",
      "Predicted Day 5: 3552.934743\n",
      "Predicted Day 6: 3568.268541\n",
      "Predicted Day 7: 3552.775660\n",
      "Predicted Day 8: 3556.261859\n",
      "Predicted Day 9: 3570.947030\n",
      "Predicted Day 10: 3543.905276\n",
      "Predicted Day 11: 3436.117255\n",
      "Predicted Day 12: 3397.177743\n",
      "Predicted Day 13: 3423.271251\n",
      "Predicted Day 14: 3423.546939\n",
      "Predicted Day 15: 3415.844338\n",
      "Predicted Day 16: 3431.356997\n",
      "Predicted Day 17: 3424.078840\n",
      "Predicted Day 18: 3413.194452\n",
      "Predicted Day 19: 3418.573441\n",
      "Predicted Day 20: 3370.533563\n",
      "Predicted Day 21: 3365.433657\n",
      "Predicted Day 22: 3455.058870\n",
      "Predicted Day 23: 3614.106758\n",
      "Predicted Day 24: 3607.111115\n",
      "Predicted Day 25: 3594.390119\n",
      "Predicted Day 26: 3577.129781\n",
      "Predicted Day 27: 3581.229537\n",
      "Predicted Day 28: 3568.077358\n",
      "Predicted Day 29: 3569.700168\n",
      "Predicted Day 30: 3586.930842\n",
      "Predicted Day 31: 3584.543137\n",
      "Predicted Day 32: 3755.929337\n",
      "Predicted Day 33: 3899.987676\n",
      "Predicted Day 34: 3910.166229\n",
      "Predicted Day 35: 3905.790432\n",
      "Predicted Day 36: 3924.967304\n",
      "Predicted Day 37: 3984.242761\n",
      "Predicted Day 38: 3984.426848\n",
      "Predicted Day 39: 3792.419597\n",
      "Predicted Day 40: 3798.796185\n",
      "Predicted Day 41: 3794.201469\n",
      "Predicted Day 42: 3809.702621\n",
      "Predicted Day 43: 3812.797251\n",
      "Predicted Day 44: 3804.196770\n",
      "Predicted Day 45: 3800.243470\n",
      "Predicted Day 46: 3722.709427\n",
      "Predicted Day 47: 3768.422837\n",
      "Predicted Day 48: 3838.686883\n",
      "Predicted Day 49: 3860.562209\n",
      "Predicted Day 50: 3871.228982\n",
      "Predicted Day 51: 3890.585281\n",
      "Predicted Day 52: 3891.892843\n",
      "Predicted Day 53: 3862.254838\n",
      "Predicted Day 54: 3846.595175\n",
      "Predicted Day 55: 3852.155728\n",
      "Predicted Day 56: 3849.394170\n",
      "Predicted Day 57: 3877.896326\n",
      "Predicted Day 58: 3977.897275\n",
      "Predicted Day 59: 3962.667184\n",
      "Predicted Day 60: 3968.213643\n",
      "Predicted Day 61: 3976.201396\n",
      "Predicted Day 62: 3995.227969\n",
      "Predicted Day 63: 4004.528101\n",
      "Predicted Day 64: 3981.056240\n",
      "Predicted Day 65: 3979.582467\n",
      "Predicted Day 66: 3967.718419\n",
      "Predicted Day 67: 3948.356288\n",
      "Predicted Day 68: 3907.492819\n",
      "Predicted Day 69: 3993.329684\n",
      "Predicted Day 70: 4008.398438\n",
      "Predicted Day 71: 4057.237273\n",
      "Predicted Day 72: 4083.230028\n",
      "Predicted Day 73: 4087.685438\n",
      "Predicted Day 74: 4124.939124\n",
      "Predicted Day 75: 4648.383801\n",
      "Predicted Day 76: 5037.641706\n",
      "Predicted Day 77: 4970.801766\n",
      "Predicted Day 78: 4979.926517\n",
      "Predicted Day 79: 5043.301324\n",
      "Predicted Day 80: 5127.740678\n",
      "Predicted Day 81: 5240.710254\n",
      "Predicted Day 82: 5220.416444\n",
      "Predicted Day 83: 5270.005985\n",
      "Predicted Day 84: 5128.922927\n",
      "Predicted Day 85: 5046.713128\n",
      "Predicted Day 86: 5083.839840\n",
      "Predicted Day 87: 5078.185213\n",
      "Predicted Day 88: 5120.512842\n",
      "Predicted Day 89: 5098.159647\n",
      "Predicted Day 90: 5217.853188\n",
      "Predicted Day 91: 5263.083470\n",
      "Predicted Day 92: 5258.436157\n",
      "Predicted Day 93: 5312.450711\n",
      "Predicted Day 94: 5286.529061\n",
      "Predicted Day 95: 5317.110774\n",
      "Predicted Day 96: 5532.230664\n",
      "Predicted Day 97: 5486.992612\n",
      "Predicted Day 98: 5422.133366\n",
      "Predicted Day 99: 5148.615064\n",
      "Predicted Day 100: 5164.293257\n",
      "Predicted Day 101: 5168.831481\n",
      "Predicted Day 102: 5154.072511\n",
      "Predicted Day 103: 5200.139911\n",
      "Predicted Day 104: 5303.777208\n",
      "Predicted Day 105: 5361.593830\n",
      "Predicted Day 106: 5618.055663\n",
      "Predicted Day 107: 5694.240788\n",
      "Predicted Day 108: 5721.422804\n",
      "Predicted Day 109: 5656.185436\n",
      "Predicted Day 110: 5863.525743\n",
      "Predicted Day 111: 5843.736843\n",
      "Predicted Day 112: 6046.744258\n",
      "Predicted Day 113: 6290.084091\n",
      "Predicted Day 114: 6805.743438\n",
      "Predicted Day 115: 7154.211105\n",
      "Predicted Day 116: 7408.366513\n",
      "Predicted Day 117: 7991.345675\n",
      "Predicted Day 118: 8053.843919\n",
      "Predicted Day 119: 7992.681116\n",
      "Predicted Day 120: 7261.673976\n",
      "Predicted Day 121: 7324.987887\n",
      "Predicted Day 122: 7933.182784\n",
      "Predicted Day 123: 7905.352392\n",
      "Predicted Day 124: 7951.782726\n",
      "Predicted Day 125: 7889.550494\n",
      "Predicted Day 126: 7701.996446\n",
      "Predicted Day 127: 7982.600085\n",
      "Predicted Day 128: 8045.556585\n",
      "Predicted Day 129: 8127.802926\n",
      "Predicted Day 130: 8783.818157\n",
      "Predicted Day 131: 8726.593734\n",
      "Predicted Day 132: 8647.500292\n",
      "Predicted Day 133: 8631.999932\n",
      "Predicted Day 134: 8351.033804\n",
      "Predicted Day 135: 8543.180028\n",
      "Predicted Day 136: 8669.521781\n",
      "Predicted Day 137: 8531.849140\n",
      "Predicted Day 138: 7850.235273\n",
      "Predicted Day 139: 7772.733708\n",
      "Predicted Day 140: 7750.936253\n",
      "Predicted Day 141: 7937.985174\n",
      "Predicted Day 142: 7939.781303\n",
      "Predicted Day 143: 7797.754123\n",
      "Predicted Day 144: 7818.804605\n",
      "Predicted Day 145: 7896.085981\n",
      "Predicted Day 146: 8035.765556\n",
      "Predicted Day 147: 8168.844200\n",
      "Predicted Day 148: 8352.473460\n",
      "Predicted Day 149: 8726.943004\n",
      "Predicted Day 150: 9094.545069\n",
      "Predicted Day 151: 9235.102400\n",
      "Predicted Day 152: 9151.756037\n",
      "Predicted Day 153: 9147.269927\n",
      "Predicted Day 154: 9352.482453\n",
      "Predicted Day 155: 9800.741986\n",
      "Predicted Day 156: 10724.060973\n",
      "Predicted Day 157: 10746.156479\n",
      "Predicted Day 158: 10846.102237\n",
      "Predicted Day 159: 11315.072996\n",
      "Predicted Day 160: 12642.838652\n",
      "Predicted Day 161: 11803.885133\n",
      "Predicted Day 162: 11678.694318\n",
      "Predicted Day 163: 11853.793263\n",
      "Predicted Day 164: 11541.807879\n",
      "Predicted Day 165: 10687.572638\n",
      "Predicted Day 166: 10303.203223\n",
      "Predicted Day 167: 11344.470673\n",
      "Predicted Day 168: 11758.298472\n",
      "Predicted Day 169: 11138.749738\n",
      "Predicted Day 170: 11414.625233\n",
      "Predicted Day 171: 11310.732716\n",
      "Predicted Day 172: 11793.359548\n",
      "Predicted Day 173: 12513.129068\n",
      "Predicted Day 174: 12648.033198\n",
      "Predicted Day 175: 11582.587112\n",
      "Predicted Day 176: 11583.788235\n",
      "Predicted Day 177: 11427.853771\n",
      "Predicted Day 178: 10856.282602\n",
      "Predicted Day 179: 10416.569918\n",
      "Predicted Day 180: 10354.931120\n",
      "Predicted Day 181: 9576.680221\n",
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      "Predicted Day 190: 9773.980207\n",
      "Predicted Day 191: 9727.460495\n",
      "Predicted Day 192: 9484.170073\n",
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      "Predicted Day 195: 9857.002947\n",
      "Predicted Day 196: 10084.360515\n",
      "Predicted Day 197: 10474.326707\n",
      "Predicted Day 198: 10784.088113\n",
      "Predicted Day 199: 10820.148292\n",
      "Predicted Day 200: 11675.017745\n",
      "Predicted Day 201: 11756.836542\n",
      "Predicted Day 202: 11710.799444\n",
      "Predicted Day 203: 11800.604710\n",
      "Predicted Day 204: 11818.598161\n",
      "Predicted Day 205: 11578.026997\n",
      "Predicted Day 206: 11377.488844\n",
      "Predicted Day 207: 11394.489609\n",
      "Predicted Day 208: 11136.764831\n",
      "Predicted Day 209: 10437.926597\n",
      "Predicted Day 210: 10010.352039\n",
      "Predicted Day 211: 10229.963191\n",
      "Predicted Day 212: 10295.587577\n",
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      "Predicted Day 217: 10045.560429\n",
      "Predicted Day 218: 10262.923566\n",
      "Predicted Day 219: 10154.798057\n",
      "Predicted Day 220: 10099.912907\n",
      "Predicted Day 221: 10345.001595\n",
      "Predicted Day 222: 10172.694292\n",
      "Predicted Day 223: 10041.924982\n",
      "Predicted Day 224: 9518.451536\n",
      "Predicted Day 225: 9534.029537\n",
      "Predicted Day 226: 9592.743416\n",
      "Predicted Day 227: 9625.980245\n",
      "Predicted Day 228: 9950.070374\n",
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      "Predicted Day 230: 10562.903487\n",
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      "Predicted Day 235: 10300.308709\n",
      "Predicted Day 236: 10220.045563\n",
      "Predicted Day 237: 10062.872373\n",
      "Predicted Day 238: 10218.994791\n",
      "Predicted Day 239: 10315.861554\n",
      "Predicted Day 240: 10341.169325\n",
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      "Predicted Day 249: 9876.761017\n",
      "Predicted Day 250: 9422.482690\n",
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      "Predicted Day 267: 8398.160270\n",
      "Predicted Day 268: 8333.279232\n",
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      "Predicted Day 296: 8809.994363\n",
      "Predicted Day 297: 8895.850726\n",
      "Predicted Day 298: 8792.176304\n",
      "Predicted Day 299: 8733.543601\n",
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      "Predicted Day 307: 8097.688990\n",
      "Predicted Day 308: 7846.672609\n",
      "Predicted Day 309: 7341.060950\n",
      "Predicted Day 310: 7241.766138\n",
      "Predicted Day 311: 7145.268901\n",
      "Predicted Day 312: 6961.172431\n",
      "Predicted Day 313: 7140.135898\n",
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      "Predicted Day 324: 7531.933091\n",
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      "Predicted Day 327: 7299.812056\n",
      "Predicted Day 328: 7209.902885\n",
      "Predicted Day 329: 7171.542521\n",
      "Predicted Day 330: 7226.158386\n",
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      "Predicted Day 332: 7088.979074\n",
      "Predicted Day 333: 7032.116359\n",
      "Predicted Day 334: 6789.439607\n",
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      "Predicted Day 336: 7147.499114\n",
      "Predicted Day 337: 7150.095230\n",
      "Predicted Day 338: 7147.617427\n",
      "Predicted Day 339: 7227.494359\n",
      "Predicted Day 340: 7503.311531\n",
      "Predicted Day 341: 7295.675537\n",
      "Predicted Day 342: 7210.193932\n",
      "Predicted Day 343: 7220.432923\n",
      "Predicted Day 344: 7191.117040\n",
      "Predicted Day 345: 7297.969223\n",
      "Predicted Day 346: 7344.891939\n",
      "Predicted Day 347: 7297.168720\n",
      "Predicted Day 348: 7198.304160\n",
      "Predicted Day 349: 7195.067520\n",
      "Predicted Day 350: 7071.733602\n",
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      "Predicted Day 353: 7429.283177\n",
      "Predicted Day 354: 7533.176286\n",
      "Predicted Day 355: 7929.153081\n",
      "Predicted Day 356: 8234.879242\n",
      "Predicted Day 357: 7897.825197\n",
      "Predicted Day 358: 7887.391183\n",
      "Predicted Day 359: 8112.616794\n",
      "Predicted Day 360: 8110.971331\n",
      "Predicted Day 361: 8102.149813\n",
      "Predicted Day 362: 8573.867672\n",
      "Predicted Day 363: 8742.948111\n",
      "Predicted Day 364: 8673.671943\n",
      "Predicted Day 365: 8852.060545\n",
      "Predicted Day 366: 3678.252268\n",
      "Predicted Day 367: 3689.840908\n",
      "Predicted Day 368: 3594.401564\n",
      "Predicted Day 369: 3544.751359\n",
      "Predicted Day 370: 3552.190711\n",
      "Predicted Day 371: 3567.524509\n",
      "Predicted Day 372: 3552.031628\n",
      "Predicted Day 373: 3555.517827\n",
      "Predicted Day 374: 3570.202998\n",
      "Predicted Day 375: 3543.161244\n",
      "Predicted Day 376: 3435.373223\n",
      "Predicted Day 377: 3396.433711\n",
      "Predicted Day 378: 3422.527218\n",
      "Predicted Day 379: 3422.802906\n",
      "Predicted Day 380: 3415.100305\n",
      "Predicted Day 381: 3430.612964\n",
      "Predicted Day 382: 3423.334808\n",
      "Predicted Day 383: 3412.450420\n",
      "Predicted Day 384: 3417.829409\n",
      "Predicted Day 385: 3369.789531\n",
      "Predicted Day 386: 3364.689625\n",
      "Predicted Day 387: 3454.314838\n",
      "Predicted Day 388: 3613.362726\n",
      "Predicted Day 389: 3606.367083\n",
      "Predicted Day 390: 3593.646087\n",
      "Predicted Day 391: 3576.385749\n",
      "Predicted Day 392: 3580.485505\n",
      "Predicted Day 393: 3567.333326\n",
      "Predicted Day 394: 3568.956136\n",
      "Predicted Day 395: 3586.186810\n",
      "Predicted Day 396: 3583.799105\n",
      "Predicted Day 397: 3755.185305\n",
      "Predicted Day 398: 3899.243644\n",
      "Predicted Day 399: 3909.422197\n",
      "Predicted Day 400: 3905.046400\n",
      "Predicted Day 401: 3924.223272\n",
      "Predicted Day 402: 3983.498729\n",
      "Predicted Day 403: 3983.682816\n",
      "Predicted Day 404: 3791.675565\n",
      "Predicted Day 405: 3798.052153\n",
      "Predicted Day 406: 3793.457437\n",
      "Predicted Day 407: 3808.958589\n",
      "Predicted Day 408: 3812.053219\n",
      "Predicted Day 409: 3803.452738\n",
      "Predicted Day 410: 3799.499438\n",
      "Predicted Day 411: 3721.965395\n",
      "Predicted Day 412: 3767.678805\n",
      "Predicted Day 413: 3837.942851\n",
      "Predicted Day 414: 3859.818176\n",
      "Predicted Day 415: 3870.484950\n",
      "Predicted Day 416: 3889.841249\n",
      "Predicted Day 417: 3891.148811\n",
      "Predicted Day 418: 3861.510806\n",
      "Predicted Day 419: 3845.851143\n",
      "Predicted Day 420: 3851.411696\n",
      "Predicted Day 421: 3848.650138\n",
      "Predicted Day 422: 3877.152293\n",
      "Predicted Day 423: 3977.153242\n",
      "Predicted Day 424: 3961.923152\n",
      "Predicted Day 425: 3967.469611\n",
      "Predicted Day 426: 3975.457364\n",
      "Predicted Day 427: 3994.483937\n",
      "Predicted Day 428: 4003.784069\n",
      "Predicted Day 429: 3980.312208\n",
      "Predicted Day 430: 3978.838435\n",
      "Predicted Day 431: 3966.974387\n",
      "Predicted Day 432: 3947.612256\n",
      "Predicted Day 433: 3906.748787\n",
      "Predicted Day 434: 3992.585651\n",
      "Predicted Day 435: 4007.654406\n",
      "Predicted Day 436: 4056.493240\n",
      "Predicted Day 437: 4082.485995\n",
      "Predicted Day 438: 4086.941406\n",
      "Predicted Day 439: 4124.195092\n"
     ]
    }
   ],
   "source": [
    "# Split the data as usual 70, 30\n",
    "price = df2.Weighted_Price\n",
    "X = price.values\n",
    "datesX = price.index\n",
    "size = int(len(X) * 0.70)\n",
    "train, test = X[0:size], X[size:len(X)]\n",
    "days_in_year = 365\n",
    "plotDates = datesX[size:len(X)]\n",
    "\n",
    "# Next we will forecast with ARIMA using 5,1,0\n",
    "differenced = difference(train, days_in_year)\n",
    "model = ARIMA(differenced, order=(5, 1, 0))\n",
    "model_fit = model.fit()\n",
    "start_index = len(differenced)\n",
    "end_index = start_index + 438\n",
    "forecast = model_fit.predict(start=start_index, end=end_index)\n",
    "\n",
    "history = [x for x in train]\n",
    "day = 1\n",
    "predicted_results = list()\n",
    "\n",
    "# store predicted results \n",
    "for yhat in forecast:\n",
    "    inverted = inverse_difference(history, yhat, days_in_year)\n",
    "    print(\"Predicted Day %d: %f\" % (day, inverted))\n",
    "    history.append(inverted)\n",
    "    predicted_results.append(inverted)\n",
    "    day += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "21c21d4e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:15.725084Z",
     "iopub.status.busy": "2021-07-30T22:41:15.724129Z",
     "iopub.status.idle": "2021-07-30T22:41:15.728658Z",
     "shell.execute_reply": "2021-07-30T22:41:15.728060Z",
     "shell.execute_reply.started": "2021-07-30T02:42:01.697276Z"
    },
    "id": "OvDj0hAb2NUV",
    "outputId": "db22ca0e-69b7-4901-fe1f-1596e46d6b58",
    "papermill": {
     "duration": 0.174762,
     "end_time": "2021-07-30T22:41:15.728821",
     "exception": false,
     "start_time": "2021-07-30T22:41:15.554059",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 19633.649\n"
     ]
    }
   ],
   "source": [
    "rmse = np.sqrt(mean_squared_error(test, predicted_results))\n",
    "print('Test RMSE: %.3f' % rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "137c8406",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:16.071422Z",
     "iopub.status.busy": "2021-07-30T22:41:16.070185Z",
     "iopub.status.idle": "2021-07-30T22:41:16.637920Z",
     "shell.execute_reply": "2021-07-30T22:41:16.638679Z",
     "shell.execute_reply.started": "2021-07-30T02:42:03.756134Z"
    },
    "id": "BXl7PAqAx_iw",
    "outputId": "4833c362-b8fe-4a5f-92b5-e449e20dbdb4",
    "papermill": {
     "duration": 0.744626,
     "end_time": "2021-07-30T22:41:16.638961",
     "exception": false,
     "start_time": "2021-07-30T22:41:15.894335",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                    D.y   No. Observations:                  655\n",
      "Model:                 ARIMA(5, 1, 0)   Log Likelihood               -4862.648\n",
      "Method:                       css-mle   S.D. of innovations            405.410\n",
      "Date:                Fri, 30 Jul 2021   AIC                           9739.296\n",
      "Time:                        22:41:16   BIC                           9770.688\n",
      "Sample:                             1   HQIC                          9751.468\n",
      "                                                                              \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.7440     21.291     -0.035      0.972     -42.474      40.986\n",
      "ar.L1.D.y      0.2159      0.039      5.529      0.000       0.139       0.292\n",
      "ar.L2.D.y      0.0256      0.040      0.643      0.520      -0.052       0.104\n",
      "ar.L3.D.y     -0.0264      0.040     -0.664      0.507      -0.105       0.052\n",
      "ar.L4.D.y      0.0662      0.040      1.662      0.097      -0.012       0.144\n",
      "ar.L5.D.y     -0.0249      0.039     -0.637      0.524      -0.101       0.052\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1           -1.7955           -0.0000j            1.7955           -0.5000\n",
      "AR.2           -0.0611           -1.9819j            1.9828           -0.2549\n",
      "AR.3           -0.0611           +1.9819j            1.9828            0.2549\n",
      "AR.4            2.2909           -0.6714j            2.3873           -0.0454\n",
      "AR.5            2.2909           +0.6714j            2.3873            0.0454\n",
      "-----------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 0\n",
      "count   655.000000\n",
      "mean     -0.088772\n",
      "std     405.730521\n",
      "min   -2280.438364\n",
      "25%    -195.521870\n",
      "50%       7.393693\n",
      "75%     194.263830\n",
      "max    2110.040288\n"
     ]
    }
   ],
   "source": [
    "print(model_fit.summary())\n",
    "# line plot of residuals\n",
    "residuals = pd.DataFrame(model_fit.resid)\n",
    "residuals.plot()\n",
    "plt.show()\n",
    "# density plot of residuals\n",
    "residuals.plot(kind='kde')\n",
    "plt.show()\n",
    "# summary stats of residuals\n",
    "print(residuals.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d6cfb280",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:16.996451Z",
     "iopub.status.busy": "2021-07-30T22:41:16.984375Z",
     "iopub.status.idle": "2021-07-30T22:41:17.226745Z",
     "shell.execute_reply": "2021-07-30T22:41:17.226166Z",
     "shell.execute_reply.started": "2021-07-30T02:42:07.831658Z"
    },
    "id": "5OLP7HpJ2gNc",
    "outputId": "15426460-b5e8-41f4-8936-cbb0b7324c56",
    "papermill": {
     "duration": 0.423049,
     "end_time": "2021-07-30T22:41:17.226926",
     "exception": false,
     "start_time": "2021-07-30T22:41:16.803877",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,10))\n",
    "plt.plot(plotDates, test, label='Expected')\n",
    "plt.plot(plotDates, predicted_results, label='Predicted')\n",
    "plt.ylabel('$ Price')\n",
    "plt.xlabel('Date')\n",
    "plt.legend(loc='best')\n",
    "plt.title('Predicted and Expected BTC Price (ARIMA)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca61f043",
   "metadata": {
    "id": "V2PObTTJgm2F",
    "papermill": {
     "duration": 0.156978,
     "end_time": "2021-07-30T22:41:17.542539",
     "exception": false,
     "start_time": "2021-07-30T22:41:17.385561",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **4. LSTM**\n",
    "Long Short-Term Memory (LSTM) models are a type of recurrent neural network capable of learning sequences of observations. This makes them a deep learning network well suited for time series forecasting. However, usually a LTSM would struggle with a time series dataset as volatile and unpredictable as Bitcoin. After a grueling process of trying to apply my data, I finally trained the model. In my final fit I used 50 epochs and the “adam” optimizer. (This model is inspired by another user's kaggle notebook)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a1c30b1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:17.867052Z",
     "iopub.status.busy": "2021-07-30T22:41:17.866274Z",
     "iopub.status.idle": "2021-07-30T22:41:23.659240Z",
     "shell.execute_reply": "2021-07-30T22:41:23.659786Z",
     "shell.execute_reply.started": "2021-07-30T02:42:11.081596Z"
    },
    "id": "LUX7AOR8godk",
    "papermill": {
     "duration": 5.959392,
     "end_time": "2021-07-30T22:41:23.659995",
     "exception": false,
     "start_time": "2021-07-30T22:41:17.700603",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.layers import Dense, LSTM, Dropout,Flatten\n",
    "from tensorflow.keras import Sequential\n",
    "from statsmodels.graphics.tsaplots import plot_acf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "336eefd5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:23.991232Z",
     "iopub.status.busy": "2021-07-30T22:41:23.990183Z",
     "iopub.status.idle": "2021-07-30T22:41:23.992923Z",
     "shell.execute_reply": "2021-07-30T22:41:23.993437Z",
     "shell.execute_reply.started": "2021-07-30T02:42:19.771033Z"
    },
    "id": "uzTzi-vngpPm",
    "papermill": {
     "duration": 0.174831,
     "end_time": "2021-07-30T22:41:23.993644",
     "exception": false,
     "start_time": "2021-07-30T22:41:23.818813",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "price = df2.Weighted_Price\n",
    "# As usual split data, 70, 30\n",
    "X = price\n",
    "size = int(len(X) * 0.7)\n",
    "train_df, test_df = X[0:size], X[size:len(X)]\n",
    "training_values = train_df.values\n",
    "training_values = np.reshape(training_values, (len(training_values), 1))\n",
    "\n",
    "# Scale the data using minMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "training_values = scaler.fit_transform(training_values)\n",
    "# assign training values\n",
    "x_train = training_values[0: len(training_values)-1]\n",
    "y_train = training_values[1: len(training_values)]\n",
    "x_train = np.reshape(x_train, (len(x_train), 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "76309d3d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:24.333402Z",
     "iopub.status.busy": "2021-07-30T22:41:24.332640Z",
     "iopub.status.idle": "2021-07-30T22:41:24.557780Z",
     "shell.execute_reply": "2021-07-30T22:41:24.556967Z",
     "shell.execute_reply.started": "2021-07-30T02:42:22.799947Z"
    },
    "id": "92eOWgQPhCrT",
    "papermill": {
     "duration": 0.396773,
     "end_time": "2021-07-30T22:41:24.557983",
     "exception": false,
     "start_time": "2021-07-30T22:41:24.161210",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# creates model\n",
    "model = Sequential()\n",
    "model.add(LSTM(10,input_shape = (None,1), activation=\"relu\", return_sequences=True))\n",
    "model.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9b7c217a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:24.892432Z",
     "iopub.status.busy": "2021-07-30T22:41:24.891756Z",
     "iopub.status.idle": "2021-07-30T22:41:24.910683Z",
     "shell.execute_reply": "2021-07-30T22:41:24.910107Z",
     "shell.execute_reply.started": "2021-07-30T02:42:25.356819Z"
    },
    "id": "kqmnA8V2hFbi",
    "papermill": {
     "duration": 0.192283,
     "end_time": "2021-07-30T22:41:24.910843",
     "exception": false,
     "start_time": "2021-07-30T22:41:24.718560",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.compile(loss=\"mean_squared_error\",optimizer=\"adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4c006659",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:25.231106Z",
     "iopub.status.busy": "2021-07-30T22:41:25.230096Z",
     "iopub.status.idle": "2021-07-30T22:41:30.157195Z",
     "shell.execute_reply": "2021-07-30T22:41:30.157698Z",
     "shell.execute_reply.started": "2021-07-30T02:42:27.443638Z"
    },
    "id": "Mn8er0VmhH4Q",
    "outputId": "abbe7ac6-8edc-4461-c810-e8f5879c5cfd",
    "papermill": {
     "duration": 5.089315,
     "end_time": "2021-07-30T22:41:30.157924",
     "exception": false,
     "start_time": "2021-07-30T22:41:25.068609",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "32/32 [==============================] - 1s 2ms/step - loss: 0.1433\n",
      "Epoch 2/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.1155\n",
      "Epoch 3/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0914\n",
      "Epoch 4/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0789\n",
      "Epoch 5/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0598\n",
      "Epoch 6/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0506\n",
      "Epoch 7/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0408\n",
      "Epoch 8/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0349\n",
      "Epoch 9/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0287\n",
      "Epoch 10/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0241\n",
      "Epoch 11/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0238\n",
      "Epoch 12/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0211\n",
      "Epoch 13/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0208\n",
      "Epoch 14/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0206\n",
      "Epoch 15/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0216\n",
      "Epoch 16/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0179\n",
      "Epoch 17/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0178\n",
      "Epoch 18/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0176\n",
      "Epoch 19/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0163\n",
      "Epoch 20/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0143\n",
      "Epoch 21/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0145\n",
      "Epoch 22/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0148\n",
      "Epoch 23/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0118\n",
      "Epoch 24/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0116\n",
      "Epoch 25/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0113\n",
      "Epoch 26/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0107\n",
      "Epoch 27/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0098\n",
      "Epoch 28/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0086\n",
      "Epoch 29/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0075\n",
      "Epoch 30/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0064\n",
      "Epoch 31/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0060\n",
      "Epoch 32/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0053\n",
      "Epoch 33/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0046\n",
      "Epoch 34/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0042\n",
      "Epoch 35/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0035\n",
      "Epoch 36/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0029\n",
      "Epoch 37/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0027\n",
      "Epoch 38/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0023\n",
      "Epoch 39/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0020\n",
      "Epoch 40/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0015\n",
      "Epoch 41/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0014\n",
      "Epoch 42/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0014\n",
      "Epoch 43/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 9.7656e-04\n",
      "Epoch 44/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 9.3774e-04\n",
      "Epoch 45/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 8.8284e-04\n",
      "Epoch 46/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 7.5940e-04\n",
      "Epoch 47/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 7.5397e-04\n",
      "Epoch 48/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 7.9490e-04\n",
      "Epoch 49/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 7.5347e-04\n",
      "Epoch 50/50\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 6.8544e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f31bb741250>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the model to the training data\n",
    "model.fit(x_train,y_train,epochs=50,batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c738c280",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:30.615336Z",
     "iopub.status.busy": "2021-07-30T22:41:30.614352Z",
     "iopub.status.idle": "2021-07-30T22:41:30.874687Z",
     "shell.execute_reply": "2021-07-30T22:41:30.875277Z",
     "shell.execute_reply.started": "2021-07-30T02:42:45.781293Z"
    },
    "id": "EyiKCF3fhQZ9",
    "papermill": {
     "duration": 0.509792,
     "end_time": "2021-07-30T22:41:30.875471",
     "exception": false,
     "start_time": "2021-07-30T22:41:30.365679",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# assign test and predicted values + reshaping + converting back from scaler\n",
    "test_values = test_df.values\n",
    "test_values = np.reshape(test_values, (len(test_values), 1))\n",
    "test_values = scaler.transform(test_values)\n",
    "test_values = np.reshape(test_values, (len(test_values), 1, 1))\n",
    "predicted_price = model.predict(test_values)\n",
    "predicted_price = np.reshape(predicted_price, (len(predicted_price), 1))\n",
    "predicted_price = scaler.inverse_transform(predicted_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a6263832",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:31.322537Z",
     "iopub.status.busy": "2021-07-30T22:41:31.321490Z",
     "iopub.status.idle": "2021-07-30T22:41:38.415501Z",
     "shell.execute_reply": "2021-07-30T22:41:38.414810Z",
     "shell.execute_reply.started": "2021-07-30T02:42:48.274209Z"
    },
    "id": "ni8H-Oi4hUzO",
    "outputId": "3af1d707-a73d-463e-e242-db06c5713e8f",
    "papermill": {
     "duration": 7.325406,
     "end_time": "2021-07-30T22:41:38.415679",
     "exception": false,
     "start_time": "2021-07-30T22:41:31.090273",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the results\n",
    "plt.figure(figsize=(15, 6))\n",
    "ax = plt.gca()  \n",
    "plt.plot(test_df.values, color = 'red', label = 'Real Price')\n",
    "plt.plot(predicted_price, color = 'green', label = 'Predicted Price')\n",
    "plt.title('BTC Price Prediction (LSTM)')\n",
    "test_df = test_df.reset_index()\n",
    "x= test_df.index\n",
    "labels = test_df['Timestamp']\n",
    "plt.xticks(x, labels, rotation = 'vertical')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('$ Price')\n",
    "plt.legend(loc=4, prop={'size': 14})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "20b6759b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:38.844142Z",
     "iopub.status.busy": "2021-07-30T22:41:38.843319Z",
     "iopub.status.idle": "2021-07-30T22:41:38.850059Z",
     "shell.execute_reply": "2021-07-30T22:41:38.849134Z",
     "shell.execute_reply.started": "2021-07-30T02:43:00.915897Z"
    },
    "id": "yQ4sOO4zh12X",
    "outputId": "1bf1ad59-8c5c-4c12-ae16-65ab1860288e",
    "papermill": {
     "duration": 0.223116,
     "end_time": "2021-07-30T22:41:38.850299",
     "exception": false,
     "start_time": "2021-07-30T22:41:38.627183",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 5200.278\n"
     ]
    }
   ],
   "source": [
    "rmse = np.sqrt(mean_squared_error(test_df.Weighted_Price.values, predicted_price))\n",
    "print('Test RMSE: %.3f' % rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d47ceda4",
   "metadata": {
    "id": "poXO9tcDxa6n",
    "papermill": {
     "duration": 0.216302,
     "end_time": "2021-07-30T22:41:39.296420",
     "exception": false,
     "start_time": "2021-07-30T22:41:39.080118",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **5. XGBOOST MODEL**\n",
    "XGBoost is one of the most popular machine learning algorithms these days. Regardless of the type of prediction task at hand; regression, or classification. XGBoost is well known to provide better solutions than other machine learning algorithms. In fact, since its inception, it has become the “state-of-the-art” machine learning algorithm to deal with structured data. However, in this situation we will be using it on time series data. Therefore, the model needed to create time series features from the datetime index - to be used alongside its target price labels when predicting. Unfortunately, in the end the model did not fair well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b23318ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:39.733685Z",
     "iopub.status.busy": "2021-07-30T22:41:39.732581Z",
     "iopub.status.idle": "2021-07-30T22:41:39.735138Z",
     "shell.execute_reply": "2021-07-30T22:41:39.735670Z",
     "shell.execute_reply.started": "2021-07-30T02:43:03.343333Z"
    },
    "id": "b6k9YsBg0oT1",
    "papermill": {
     "duration": 0.224543,
     "end_time": "2021-07-30T22:41:39.735851",
     "exception": false,
     "start_time": "2021-07-30T22:41:39.511308",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# splitting data again, 70, 30\n",
    "df5 = df2.copy()\n",
    "X = df5\n",
    "size = int(len(X) * 0.7)\n",
    "data_train, data_test = X[0:size], X[size:len(X)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6cf9229c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:40.242640Z",
     "iopub.status.busy": "2021-07-30T22:41:40.241498Z",
     "iopub.status.idle": "2021-07-30T22:41:40.250661Z",
     "shell.execute_reply": "2021-07-30T22:41:40.251208Z",
     "shell.execute_reply.started": "2021-07-30T02:43:05.400824Z"
    },
    "id": "_Ef1R0lU034Q",
    "papermill": {
     "duration": 0.295654,
     "end_time": "2021-07-30T22:41:40.251411",
     "exception": false,
     "start_time": "2021-07-30T22:41:39.955757",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# a method to create a variety of features from a time series df\n",
    "def create_features(df, label=None):\n",
    "    df['date'] = df.index\n",
    "    df['hour'] = df['date'].dt.hour\n",
    "    df['dayofweek'] = df['date'].dt.dayofweek\n",
    "    df['quarter'] = df['date'].dt.quarter\n",
    "    df['month'] = df['date'].dt.month\n",
    "    df['year'] = df['date'].dt.year\n",
    "    df['dayofyear'] = df['date'].dt.dayofyear\n",
    "    df['dayofmonth'] = df['date'].dt.day\n",
    "    df['weekofyear'] = df['date'].dt.weekofyear\n",
    "    X = df[['hour','dayofweek','quarter','month','year',\n",
    "           'dayofyear','dayofmonth','weekofyear']]\n",
    "    if label:\n",
    "        y = df[label]\n",
    "        return X, y\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2ad83709",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:40.687586Z",
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    },
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    "outputId": "afc8517f-f1b9-450f-9f1d-8219ae229dbc",
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     "exception": false,
     "start_time": "2021-07-30T22:41:40.474530",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "# assigning training and testing, features and labels (price)\n",
    "X_train, y_train = create_features(data_train, label='Weighted_Price')\n",
    "X_test, y_test = create_features(data_test, label='Weighted_Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7e03fd5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:41.169345Z",
     "iopub.status.busy": "2021-07-30T22:41:41.168482Z",
     "iopub.status.idle": "2021-07-30T22:41:41.462410Z",
     "shell.execute_reply": "2021-07-30T22:41:41.463244Z",
     "shell.execute_reply.started": "2021-07-30T02:43:20.526126Z"
    },
    "id": "0VKBf4kPxfWl",
    "outputId": "978fb9d6-8969-4667-b3ad-51bb0ceefeba",
    "papermill": {
     "duration": 0.517262,
     "end_time": "2021-07-30T22:41:41.463525",
     "exception": false,
     "start_time": "2021-07-30T22:41:40.946263",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[22:41:41] WARNING: ../src/objective/regression_obj.cu:171: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(alpha=10, base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=0.3, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "             min_child_weight=10, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=100, n_jobs=4, num_parallel_tree=1,\n",
       "             objective='reg:linear', random_state=0, reg_alpha=10, reg_lambda=1,\n",
       "             scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "             validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import XGBoost, then create model, and fit it to the training data\n",
    "import xgboost as xgb\n",
    "from xgboost import plot_importance, plot_tree\n",
    "model =  xgb.XGBRegressor(objective ='reg:linear',min_child_weight=10, booster='gbtree', colsample_bytree = 0.3, learning_rate = 0.1,\n",
    "                max_depth = 5, alpha = 10, n_estimators = 100)\n",
    "model.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], early_stopping_rounds=50, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "349b6285",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:41.915685Z",
     "iopub.status.busy": "2021-07-30T22:41:41.914457Z",
     "iopub.status.idle": "2021-07-30T22:41:41.928703Z",
     "shell.execute_reply": "2021-07-30T22:41:41.929302Z",
     "shell.execute_reply.started": "2021-07-30T02:43:24.603143Z"
    },
    "id": "hZUcsbRvzcHt",
    "outputId": "3827ce8f-b7e3-4c43-8500-d309c9ee4570",
    "papermill": {
     "duration": 0.232402,
     "end_time": "2021-07-30T22:41:41.929502",
     "exception": false,
     "start_time": "2021-07-30T22:41:41.697100",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# assign predictions to data_test and then data_all\n",
    "data_test['Weighted_Price_Prediction'] = model.predict(X_test)\n",
    "data_all = pd.concat([data_test, data_train], sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4ffddeac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-30T22:41:42.387623Z",
     "iopub.status.busy": "2021-07-30T22:41:42.386429Z",
     "iopub.status.idle": "2021-07-30T22:41:42.763642Z",
     "shell.execute_reply": "2021-07-30T22:41:42.762995Z",
     "shell.execute_reply.started": "2021-07-30T02:43:41.29557Z"
    },
    "id": "fdsI-5r62mqd",
    "outputId": "6eb541f4-9f06-47ee-9761-40d3d3969b7c",
    "papermill": {
     "duration": 0.620538,
     "end_time": "2021-07-30T22:41:42.763804",
     "exception": false,
     "start_time": "2021-07-30T22:41:42.143266",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Timestamp'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_all[['Weighted_Price','Weighted_Price_Prediction']].plot(figsize=(15, 5))"
   ]
  },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>dayofweek</th>\n",
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       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>dayofmonth</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>Weighted_Price_Prediction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
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       "      <th>2020-01-18</th>\n",
       "      <td>8891.207561</td>\n",
       "      <td>8895.987338</td>\n",
       "      <td>8886.848700</td>\n",
       "      <td>8890.932203</td>\n",
       "      <td>2.555948</td>\n",
       "      <td>22739.596158</td>\n",
       "      <td>8891.817032</td>\n",
       "      <td>2020-01-18</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>7344.434082</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-19</th>\n",
       "      <td>8846.440862</td>\n",
       "      <td>8852.059917</td>\n",
       "      <td>8841.301460</td>\n",
       "      <td>8846.728456</td>\n",
       "      <td>6.109492</td>\n",
       "      <td>53655.111012</td>\n",
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       "      <td>2020-01-19</td>\n",
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       "      <th>2020-01-20</th>\n",
       "      <td>8644.165698</td>\n",
       "      <td>8648.276386</td>\n",
       "      <td>8640.256435</td>\n",
       "      <td>8644.101508</td>\n",
       "      <td>3.071715</td>\n",
       "      <td>26529.821802</td>\n",
       "      <td>8644.708000</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>7250.313965</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-21</th>\n",
       "      <td>8645.480195</td>\n",
       "      <td>8649.571579</td>\n",
       "      <td>8642.046356</td>\n",
       "      <td>8645.604819</td>\n",
       "      <td>2.360702</td>\n",
       "      <td>20386.559122</td>\n",
       "      <td>8646.193567</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>7194.753906</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>8665.194124</td>\n",
       "      <td>8668.595431</td>\n",
       "      <td>8662.189026</td>\n",
       "      <td>8665.453755</td>\n",
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       "      <td>18933.853843</td>\n",
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       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
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      "text/plain": [
       "                   Open         High          Low        Close  Volume_(BTC)  \\\n",
       "Timestamp                                                                      \n",
       "2020-01-18  8891.207561  8895.987338  8886.848700  8890.932203      2.555948   \n",
       "2020-01-19  8846.440862  8852.059917  8841.301460  8846.728456      6.109492   \n",
       "2020-01-20  8644.165698  8648.276386  8640.256435  8644.101508      3.071715   \n",
       "2020-01-21  8645.480195  8649.571579  8642.046356  8645.604819      2.360702   \n",
       "2020-01-22  8665.194124  8668.595431  8662.189026  8665.453755      2.186239   \n",
       "\n",
       "            Volume_(Currency)  Weighted_Price       date  hour  dayofweek  \\\n",
       "Timestamp                                                                   \n",
       "2020-01-18       22739.596158     8891.817032 2020-01-18     0          5   \n",
       "2020-01-19       53655.111012     8847.166822 2020-01-19     0          6   \n",
       "2020-01-20       26529.821802     8644.708000 2020-01-20     0          0   \n",
       "2020-01-21       20386.559122     8646.193567 2020-01-21     0          1   \n",
       "2020-01-22       18933.853843     8665.685486 2020-01-22     0          2   \n",
       "\n",
       "            quarter  month  year  dayofyear  dayofmonth  weekofyear  \\\n",
       "Timestamp                                                             \n",
       "2020-01-18        1      1  2020         18          18           3   \n",
       "2020-01-19        1      1  2020         19          19           3   \n",
       "2020-01-20        1      1  2020         20          20           4   \n",
       "2020-01-21        1      1  2020         21          21           4   \n",
       "2020-01-22        1      1  2020         22          22           4   \n",
       "\n",
       "            Weighted_Price_Prediction  \n",
       "Timestamp                              \n",
       "2020-01-18                7344.434082  \n",
       "2020-01-19                7306.523438  \n",
       "2020-01-20                7250.313965  \n",
       "2020-01-21                7194.753906  \n",
       "2020-01-22                6860.525879  "
      ]
     },
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    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
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    {
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     "text": [
      "Test RMSE: 18484.329\n"
     ]
    }
   ],
   "source": [
    "rmse = np.sqrt(mean_squared_error(data_test['Weighted_Price'], data_test['Weighted_Price_Prediction']))\n",
    "print('Test RMSE: %.3f' % rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a472f1f6",
   "metadata": {
    "id": "erW49AMG0PvY",
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   },
   "source": [
    "## **6. Conclusion**\n",
    "The final results of the experiment are plotted below. The Long Short-Term Memory model proves to be the most effective when handling volatile and hard-to-predict data like Bitcoin prices. This Bitcoin dataset consisted of extremely volatile and abnormal time series data. Therefore, I was unsurprised to find that the models had a difficult time predicting the most recent 439 days of prices. In addition, I acknowledge that I could’ve picked better and more informed parameters for some of these models and libraries in order to make the results more fair. However, I think the LSTM model’s low RMSE proves how powerful neural networks can be in Machine Learning. I’d love to explore RNN’s in financial time series data more in the future!\n"
   ]
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     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Final graph for the RMSE's of each model\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "modelz = ['Prophet', 'ARIMA', 'LSTM', 'XGBoost']\n",
    "nums = [24810,19633,8950, 18483]\n",
    "ax.bar(modelz,nums)\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('RMSE')\n",
    "plt.title('RMSE of the Models')\n",
    "plt.show()\n",
    "plt.show()"
   ]
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