This repository contains implementations of various time series forecasting models aimed at predicting future values in a data sequence over time. These models are applicable to domains such as finance, weather forecasting, and demand planning in supply chain management. My goal is to provide a scalable, efficient, and user-friendly solution for time series analysis for anyone interested in this topic.
- Multiple Forecasting Models: Implementation of ARIMA, SARIMA, Prophet, RNN, and more.
- Data Preprocessing Tools: Utilities for cleaning and preparing time series data.
- Evaluation Framework: Scripts for model evaluation with metrics like MAE, RMSE, and MAPE.
- Visualization Tools: Functions for plotting data, forecasts, and evaluation metrics.
- Example Datasets: Includes sample datasets for demonstration purposes.
- Python 3.1+
- pip and virtualenv
# Clone the repository
git clone https://github.com/mjbrv/Time_Series_Forecasting.git
# Navigate to the project directory
cd Time_Series_Forecasting
## Optional
# Create a virtual environment
virtualenv venv
# Activate the virtual environment
# On Windows
.\venv\Scripts\activate
# On Unix or MacOS
source venv/bin/activate