tabpfn-time-series 1.0.9
Zero-shot time series forecasting with TabPFNv2
Requires Python: >=3.10

# TabPFN-TS

> Zero-Shot Time Series Forecasting with TabPFNv2

[![PyPI version](https://badge.fury.io/py/tabpfn-time-series.svg)](https://badge.fury.io/py/tabpfn-time-series)
[![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PriorLabs/tabpfn-time-series/blob/main/quickstart.ipynb)
[![Discord](https://img.shields.io/discord/1285598202732482621?color=7289da&label=Discord&logo=discord&logoColor=ffffff)](https://discord.com/channels/1285598202732482621/)
[![arXiv](https://img.shields.io/badge/arXiv-2501.02945-<COLOR>.svg)](https://arxiv.org/abs/2501.02945v3)

## 📌 News
- **27-05-2025**: 📝 New **[paper](https://arxiv.org/abs/2501.02945v3)** version and **v1.0.0** release! Strong [GIFT-EVAL](https://huggingface.co/spaces/Salesforce/GIFT-Eval) results, new AutoSeasonalFeatures, improved CalendarFeatures.
- **27-01-2025**: 🚀 Ranked _**1st**_ on [GIFT-EVAL](https://huggingface.co/spaces/Salesforce/GIFT-Eval) benchmark<sup>[1]</sup>!
- **10-10-2024**: 🚀 TabPFN-TS [paper](https://arxiv.org/abs/2501.02945v2) accepted to NeurIPS 2024 [TRL](https://table-representation-learning.github.io/NeurIPS2024/) and [TSALM](https://neurips-time-series-workshop.github.io/) workshops!

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

## ✨ Introduction
We demonstrate that the tabular foundation model **[TabPFNv2](https://github.com/PriorLabs/TabPFN)**, combined with lightweight feature engineering, enables zero-shot time series forecasting for both point and probabilistic tasks. On the **[GIFT-EVAL](https://huggingface.co/spaces/Salesforce/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**.

![How it works](docs/tabpfn-ts-method-overview.png)

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](https://arxiv.org/abs/2501.02945v3).
 <!-- and our [poster](docs/tabpfn-ts-neurips-poster.pdf) (presented at NeurIPS 2024 TRL and TSALM workshops). -->

## 👉 **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.
- **Native covariate support**: it seamlessly incorporates external features (weather, holidays, promotions) with no preprocessing required.

On top of that, thanks to **[tabpfn-client](https://github.com/automl/tabpfn-client)** from **[Prior Labs](https://priorlabs.ai)**, 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:

```bash
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:

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

## 🚀 Getting Started

| I want to... | Notebook |
|--------------|----------|
| Use it on my project | [**quickstart.ipynb**](examples/quickstart.ipynb) |
| Understand how it works | [**how-it-works.ipynb**](examples/how-it-works.ipynb) |

Additionally, we have provided more example usage in the [examples](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](https://github.com/priorlabs/tabpfn/blob/main/TELEMETRY.md) 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](https://priorlabs.ai/privacy_policy/).

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

```bash
export TABPFN_DISABLE_TELEMETRY=1
```
---