SeriesPFN: Unifying Time-Series Classification and Extrinsic Regression through End-to-End In-Context Learning

Time-series classification (TSC) and extrinsic regression (TSER) predict a label or a continuous value from an entire time series, respectively. Unlike conventional approaches that require training a separate model for each dataset, pretrained foundation models such as Prior-Data Fitted Networks (PFNs) enable prediction on unseen tasks through in-context learning. They condition on a task's labeled training examples to predict targets for new series without updating their parameters. Two-stage PFNs achieve strong performance across both tasks, but rely on separate pipelines per task type and incur substantial inference cost. End-to-end time-series PFNs instead predict directly from raw series, but existing methods address classification only and trail the strongest approaches in accuracy. We introduce SeriesPFN, the first end-to-end PFN trained jointly for TSC and TSER. We develop a unified prior that generates classification and regression tasks through a shared construction, with process parameters varying between classes for classification and continuously with the target for regression. With a single checkpoint, SeriesPFN achieves the best mean accuracy rank on the UCR112 and UEA26 classification benchmarks and the second-best mean RMSE rank on the TSER62 regression benchmark. Runtime comparisons place SeriesPFN on the performance-efficiency frontier across all three benchmarks.