Time Series Analysis Project

Overview

This repository contains code and documentation for a time series analysis project. The goal of this project is to analyze temporal patterns in the given dataset and derive meaningful insights. The time series analysis involves techniques for understanding, modeling, and forecasting time-dependent data.

Table of Contents

• Introduction
• Project Structure
• Requirements
• Data
• Acknowledgments

Introduction

Time series analysis is a crucial aspect of data science, where the primary focus is on understanding and extracting patterns from data points that are ordered chronologically. This project employs various time series analysis techniques to uncover trends, seasonality, and anomalies in the provided dataset.

Project Structure

The project is organized into the following :

• code: Contains all the Python scripts and Jupyter notebooks used for data analysis and modeling time_series_analysis.ipynb.
• data: Stores the raw and processed datasets long_data_.csv.
• docs: Documentation files, including this README.md.

Requirements

To run the code in this project, you need the following dependencies:

• Python 3.11
• Jupyter Notebooks (optional but recommended)

Install the required Python packages using the following command:

pip install -r requirements.txt

Data

This Time Series Analysis uses the dataset provied by kaggle about POWER CONSUMPTION IN INDIA (2019 - 2020) which is in long_data_.csv file

Acknowledgments

• pandas: Wes McKinney and contributors (https://github.com/pandas-dev/pandas/graphs/contributors)
• prophet: Facebook, Inc. (https://github.com/facebook/prophet/graphs/contributors)
• matplotlib: John D. Hunter and contributors (https://matplotlib.org/stable/credits.html)
• scikit-learn: Contributors to scikit-learn (https://scikit-learn.org/stable/about.html#credits)
• plotly: Plotly Technologies Inc. and contributors (https://github.com/plotly/plotly.py/graphs/contributors)