tabpfn-time-series 1.0.7

Zero-shot time series forecasting with TabPFNv2

TabPFN-TS

Zero-Shot Time Series Forecasting with TabPFNv2

📌 News

• 27-05-2025: 📝 New paper version and v1.0.0 release! Strong GIFT-EVAL results, new AutoSeasonalFeatures, improved CalendarFeatures.
• 27-01-2025: 🚀 Ranked 1st on GIFT-EVAL benchmark^[1]!
• 10-10-2024: 🚀 TabPFN-TS paper accepted to NeurIPS 2024 TRL and TSALM workshops!

[1] Last checked on: 10/03/2025

✨ Introduction

We demonstrate that the tabular foundation model TabPFNv2, 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.

📖 How does it work?

Our work proposes to frame univariate time series forecasting as a tabular regression problem.

Concretely, we:

1. Transform a time series into a table
2. Extract features and add them to the table
3. Perform regression on the table using TabPFNv2
4. Use regression results as time series forecasting outputs

For more details, please refer to our paper.

👉 Why give us a try?

• Zero-shot forecasting: this method is extremely fast and requires no training, making it highly accessible for experimenting with your own problems.
• Point and probabilistic forecasting: it provides accurate point forecasts as well as probabilistic forecasts.
• Support for exogenous variables: if you have exogenous variables, this method can seemlessly incorporate them into the forecasting model.

On top of that, thanks to tabpfn-client from Prior Labs, you won't even need your own GPU to run fast inference with TabPFNv2. 😉 We have included tabpfn-client as the default engine in our implementation.

⚙️ Installation

You can install the package via pip:

pip install tabpfn-time-series

For Developers

To install the package in editable mode with all development dependencies, run the following command in your terminal:

pip install -e ".[dev]"
# or with uv
uv pip install -e ".[dev]"

How to use it?

The demo should explain it all. 😉

Additionally, we have provided example usage in the examples directory.

📊 Anonymous Telemetry

This project collects anonymous usage telemetry by default.

The data is used exclusively to help us understand how the library is being used and to guide future improvements.

• No personal data is collected
• No code, model inputs, or outputs are ever sent
• Data is strictly anonymous and cannot be linked to individuals

What we collect

We only collect high-level, non-identifying information such as:

• Package version
• Python version
• How often fit and inference are called, including simple metadata like the dimensionality of the input and the type of task (e.g., classification vs. regression) (:warning: never the data itself)

See the Telemetry documentation for the full details of events and metadata.

This data is processed in compliance with the General Data Protection Regulation (GDPR) principles of data minimization and purpose limitation.

For more details, please see our Privacy Policy.

How to opt out

If you prefer not to send telemetry, you can disable it by setting the following environment variable:

export TABPFN_DISABLE_TELEMETRY=1

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