TFB

Abstract

1. Introduction

TFB is an open-source library designed for time series forecasting researchers.

The below figure provides a visual overview of TFB's pipeline.

The table below provides a visual overview of how TFB's key features compare to other libraries for time series forecasting.

2. Objective

[PVLDB 2024 Best Paper Nomination] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods

This project demonstrates how Shell, Deep Learning can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Linux / macOS terminal or WSL on Windows
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/decisionintelligence/TFB.git
cd TFB

1. Installation:
2. From PyPI
3. From Docker
4. Data preparation:
5. Train and evaluate model:

pip install -r requirements.txt

docker build . -t tfb:latest

docker run -it -v $(pwd)/:/app/ tfb:latest bash

sh ./scripts/multivariate_forecast/ILI_script/DLinear.sh

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by decisionintelligence and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.