Time Series Forecasting Project

Overview

This project focuses on forecasting future values of a time series dataset using the ARIMA model. It includes scripts for training the model and making predictions.

Project Structure

         time-series/ 
                    │ 
                    ├── data/ # Data files 
                            │ 
                            └── time_series.csv # Example time series dataset 
                    ├──src/ 
                          │ 
                          ├── model.py # Model definition 
                          │ 
                          ├── train.py # Training script 
                          │ 
                          ├── predict.py # Prediction script 
                          │ 
                          └── utils.py # Utility functions 
                    ├──tests/ # Test scripts 
                            │ 
                            └── test_model.py # Unit tests for the model 
                    ├── requirements.txt # Dependencies 
                    └── README.md # Project documentation

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/time-series.git
   cd time-series
   
   

2. Install the required packages:

     pip install -r requirements.txt
   

Usage

1. Prepare your time series data in the /data directory and name it time_series.csv.
2. Train the model: python src/train.py
3. Make forecasts: python src/predict.py

Testing

Run the unit tests to ensure everything is functioning properly:

  python -m unittest discover -s tests