From Tables to Time: How TabPFN-v2 Outperforms Specialized Time Series Forecasting Models
This work addresses time series forecasting for practitioners by showing that a compact, tabular foundation model can outperform specialized models, though it is incremental as it adapts an existing model to a new application.
The paper tackles time series forecasting by applying TabPFN-v2, a general tabular foundation model, to this domain, achieving top rank on the GIFT-Eval leaderboard for both point and probabilistic forecasting tasks.
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.