Sequence to sequence (seq2seq) models can learn representations from sequence data in an unsupervised manner by designing appropriate learning objectives, such as reconstruction and context prediction. Although feature-based time series clustering methods are robust to noise and outliers, and can reduce the dimensionality of the data, they typically rely on domain knowledge to manually construct high-quality features. It has been widely applied to genome data, anomaly detection, and in general, in any domain where pattern detection is important. Time series clustering is an essential unsupervised technique in cases when category information is not available. AuthorFeedback Bibtex MetaReview Metadata Paper Reviews Supplemental
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