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.
Structural Classification of Locally Stationary Time Series Based on Second-order Characteristics
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abstract
Time series classification is crucial for numerous scientific and engineering applications. In this article, we present a numerically efficient, practically competitive, and theoretically rigorous classification method for distinguishing between two classes of locally stationary time series based on their time-domain, second-order characteristics. Our approach builds on the autoregressive approximation for locally stationary time series, combined with an ensemble aggregation and a distance-based threshold for classification. It imposes no requirement on the training sample size, and is shown to achieve zero misclassification error rate asymptotically when the underlying time series differ only mildly in their second-order characteristics. The new method is demonstrated to outperform a variety of state-of-the-art solutions, including wavelet-based, tree-based, convolution-based methods, as well as modern deep learning methods, through intensive numerical simulations and a real EEG data analysis for epilepsy classification.
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2026 1verdicts
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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.