A 4.5M-parameter transformer meta-trained on synthetic VARX-generated classification tasks achieves state-of-the-art ROC AUC on the UCR time series classification benchmark via in-context learning with no per-dataset training.
Pre-trained forecasting models: Strong zero-shot feature extractors for time series classification, 2025 a
2 Pith papers cite this work. Polarity classification is still indexing.
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Aionoscope shows that time-series representations recover coarse signal types reliably but expose dense latent states like phase and amplitude much less reliably, with best dense-probe R² at 0.689 versus oracle 0.999.
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TimEE: End-to-end Time Series Classification via In-Context Learning
A 4.5M-parameter transformer meta-trained on synthetic VARX-generated classification tasks achieves state-of-the-art ROC AUC on the UCR time series classification benchmark via in-context learning with no per-dataset training.
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Aionoscope: Debugging Latent-State Accessibility in Time-Series Representations
Aionoscope shows that time-series representations recover coarse signal types reliably but expose dense latent states like phase and amplitude much less reliably, with best dense-probe R² at 0.689 versus oracle 0.999.