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Multi-source Transfer Learning of Time Series with a Shapelet-based Distance Measure

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

Transfer learning is an effective technique for addressing data scarcity in deep learning for time series classification, but its success depends on the selection of source datasets. Conventional transferability estimation methods are often computationally expensive, as they require fully pre-training a model on each potential source dataset to assess its suitability. This paper introduces a novel, training-free source selection method named Shapelet Matching. Our approach first identifies discriminative shapelets from the target and potential source datasets. Then, Shapelet Matching quantifie

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