Time-Series-Prediction

This repository contains my implementations for time series prediction task.

Requirements

• Python 3.6
• PyTorch 1.6.0
• Tensorboard 1.15.0
• XGBoost 1.2.0
• Sklearn 0.19.1
• Numpy 1.16.2
• Tqdm 4.49.0

Introduction

Project

The project structure is below:

.
├── PyTorch
│   ├── LSTNet
│   │   ├── test.py
│   │   ├── layers.py
│   │   └── train.py
│   └── utils
│       ├── param_parser.py
│       └── data_helpers.py
├── data
│   ├── Train / Validation /Test_sample.json
│   ├── Train / Validation / Test_BOW_sample.json
│   └── Train / Validation / Test_pairwise_sample.json
├── LICENSE
├── README.md
└── requirements.txt

Data

See data format in /data folder which including the data sample files. For example, train_sample.json is like:

{"id": "6", "content": ["year", "ruined", "summer", "vacation-a", "two-week", "vacation", "wife", "family", "cabin", "lake", "northern", "ontario", "located", "boundary", "canada-by", "bringing", "modern", "convenience", "wa", "convenient", "good", "ipad", "admiring", "beauty", "nature", "checked", "e-mail", "paddling", "canoe", "twitter", "feed", "devouring", "great", "amusing", "stuck", "workday", "diet", "newspaper", "morning", "wa", "problem", "wa", "behaving", "office", "sticking", "unending", "news", "cycle", "body", "wa", "vacation", "head", "wasnt", "year", "made", "mind", "social", "medium", "experiment", "reverse", "withdrawal", "internet", "manage", "unplug", "knew", "wouldnt", "easy", "im", "good", "self-denial", "wa", "determined", "started", "physical", "restraint", "handing", "ipad", "wife", "helpfully", "announced", "wa", "read", "book", "club", "inclined", "relinquish", "tablet", "moment", "stroke", "luck", "cell", "phone", "signal", "canadian", "cabin", "wa", "spottier", "past", "making", "attempt", "cheating", "experience", "frustration", "wa", "trapped", "forced", "comply", "good", "intention", "largely", "cut", "e-mail", "twitter", "favorite", "newspaper", "website", "connect", "world", "radio-and", "radio", "listen", "choice", "planned", "read", "book", "experienced", "criminal", "plot", "street", "los", "angeles", "cutthroat", "battle", "cancer", "lab", "psyche", "london", "social", "butterfly", "magazine", "read", "im", "claiming", "cut", "internet", "completely", "day", "biked", "nearest", "town", "reward", "sat", "park", "bench", "front", "public", "library", "wi-fi", "back", "cabin", "suffered", "slow", "dial-up", "connection", "day", "check", "e-mail", "tale", "self-denial", "ha", "happy", "ending-for", "determination", "deep", "breathing", "strong", "support", "wife", "succeeded", "vacation", "struggle", "internet", "realizing", "finally", "wa", "ipad", "wa", "problem", "knew", "passed", "starbucks", "wife", "asked", "wanted", "stop", "wi-fi", "dont", "sound", "pleased", "return", "post-vacation", "situation", "test", "begin", "stay", "wagon", "im", "back", "work", "time", "compulsion", "whats", "overwhelming", "crucial", "livelihood", "intention", "giving", "membership", "cult", "immediacy", "hope", "resist", "temptation", "reflexively", "check", "e-mail", "minute", "lead", "long", "im", "checking", "twitter", "feed", "website", "vacation", "supposed", "reset", "brain", "productive", "hoping", "worked"], "question": ["doe", "underlined", "word", "restraint"], "pos_text": ["calm", "controlled", "behavior"], "neg_text": ["relaxing", "move", "strong", "determination", "unshakable", "faith"], "diff": 0.550373134328}

• "id": just the id.
• "content": the word segment of the content.
• "question": The word segment of the question.
• "pos_text": The word segment of the correct option.
• "neg_text": The word segment of the wrong options.
• "diff": The difficulty of the question.

Data Format

This repository can be used in other similiar datasets in two ways:

1. Modify your datasets into the same format of the sample.
2. Modify the data preprocessing code in data_helpers.py.

Anyway, it should depend on what your data and task are.

Usage

See Usage

Network Structure

The framework of LSTNet:

Reference

If you want to follow the paper or utilize the code, please note the following info in your work:

• Model LSTNet

@inproceedings{lai2018modeling,
  title={Modeling long-and short-term temporal patterns with deep neural networks},
  author={Lai, Guokun and Chang, Wei-Cheng and Yang, Yiming and Liu, Hanxiao},
  booktitle={The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
  pages={95--104},
  year={2018}
}

About Me

黄威，Randolph

SCU SE Bachelor; USTC CS Ph.D.

Email: chinawolfman@hotmail.com

My Blog: randolph.pro

LinkedIn: randolph's linkedin