From Tables to Time: How TabPFN-v2 Outperforms Specialized Time Series Forecasting Models

• URL: http://arxiv.org/abs/2501.02945v3
• Date: Mon, 26 May 2025 15:25:31 GMT
• Title: From Tables to Time: How TabPFN-v2 Outperforms Specialized Time Series Forecasting Models
• Authors: Shi Bin Hoo, Samuel Müller, David Salinas, Frank Hutter,
• Abstract summary: We introduce TabPFN-TS, a simple method that combines TabPFN-v2 with lightweight feature engineering to enable both point and probabilistic forecasting.<n>Despite its simplicity and compact size (11M parameters), TabPFN-TS achieves top rank on the public GIFT-Eval leaderboard in both forecasting tasks.
• Score: 40.19199376033612
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation models have become increasingly popular for forecasting due to their ability to provide predictions without requiring a lot of training data. In this work, we demonstrate how TabPFN-v2, a general tabular foundation model, can be effectively applied to time series forecasting. We introduce TabPFN-TS, a simple method that combines TabPFN-v2 with lightweight feature engineering to enable both point and probabilistic forecasting. Despite its simplicity and compact size (11M parameters), TabPFN-TS achieves top rank on the public GIFT-Eval leaderboard in both forecasting tasks. Through ablation studies, we investigate factors contributing to this surprising effectiveness, especially considering TabPFN-v2 was pretrained solely on synthetic tabular data with no exposure to time series. Our results highlights the potential of tabular foundation models like TabPFN-v2 as a valuable new approach for time series forecasting. Our implementation is available at https://github.com/PriorLabs/tabpfn-time-series.

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