3 papers
cs.LG2026
TimEE: End-to-end Time Series Classification via In-Context Learning
Jaris Küken, Shi Bin Hoo, Martin Mráz +2
Time series classification (TSC) is dominated by a two-stage paradigm: train a feature encoder -- either from scratch on the target dataset or via pretraining on large corpora -- a…
cs.LG2026
TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
Léo Grinsztajn, Klemens Flöge, Oscar Key +23
The first tabular foundation model, TabPFN, and its successor TabPFNv2 have impacted tabular AI substantially, with dozens of methods building on it and hundreds of applications ac…
cs.LG2026
From Tables to Time: Extending TabPFN-v2 to Time Series Forecasting
Shi Bin Hoo, Samuel Müller, David Salinas +1
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…