From Tables to Time: Extending TabPFN-v2 to Time Series Forecasting

Understand

Recent progress in foundation models has enabled strong zero-shot performance for time series forecasting.

• In this work, we show that such capabilities can also emerge from tabular foundation models.
• We introduce TabPFN-TS, a simple method that treats forecasting as a tabular regression problem by combining lightweight temporal featurization with the pretrained TabPFN-v2.
• This formulation requires no time-series-specific pretraining and naturally supports both univariate and covariate-informed forecasting.