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 time scales%2C which can fail to generalize to longer sequences. This work introduces a textit{compositional representation learning} approach trained on statistically coherent components extracted from sequential data. Based on a multi-scale change space%2C an unsupervised approach is proposed to segment the sequential data into chunks with similar statistical properties. A sequence-based encoder model is trained in a multi-task setting to learn compositional representations from these temporal components for time series classification. We demonstrate its effectiveness through extensive experiments on publicly available time series classification benchmarks. Evaluating the coherence of segmented components shows its competitive performance on the unsupervised segmentation task.?quality=80&w=800)
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