Tabpfn Time Series

Abstract

1. Introduction

We demonstrate that the tabular foundation model TabPFN, combined with lightweight feature engineering, enables zero-shot time series forecasting for both point and probabilistic tasks. On the GIFT-EVAL benchmark, our method achieves performance on par with top-tier models across both evaluation metrics.

As of v1.3.0 the package uses TabPFN-3.5 by default — a tabular foundation model pretrained purely on synthetic data. The finetuned TabPFN-TS-3 checkpoint from the TabPFN-3 report stays available.

2. Objective

Zero-shot Time Series Forecasting with TabPFN (work accepted at NeurIPS 2024 TRL and TSALM workshops)

This project demonstrates how Python 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.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

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

pip install tabpfn-time-series

from tabpfn_time_series import TabPFNTSPipeline

pipeline = TabPFNTSPipeline()  # uses the cloud client by default — no GPU needed
predictions = pipeline.predict_df(context_df, prediction_length=24)

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 PriorLabs and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.