TimEE: End-to-end Time Series Classification via In-Context Learning

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—and then fit a task-specific classifier on top. While effective, this decoupling optimizes representation learning independently of the classification objective, requires per-dataset training, and prevents the model from exploiting label information during inference. We introduce TIMEE, a 4.5M-parameter foundation model for end-to-end TSC via in-context learning. Given a labeled support set and a query time series, TIMEE outputs a predicted class distribution directly, with no encoder trained or classifier fitted on the target dataset. Following the prior-data fitted network (PFN) framework, TIMEE is meta-trained exclusively on synthetic TSC tasks, where each task contains time series with distinct class identities arising from structured distributional shifts in the generative process. Despite seeing no real time series during pre-training, TIMEE achieves the best mean rank in ROC AUC on the 128-dataset UCR archive, statistically tied with the strongest supervised baselines and ahead of every foundation model. It is also the best-calibrated and fastest of all compared methods. To our knowledge, TIMEE is the first purely synthetic-pretrained model to reach state-of-the-art performance. These results establish end-to-end ICL with synthetic priors as a compelling, largely unexplored direction for TSC, with scaling, prior design, and richer generation mechanisms as natural avenues for improvement.