Concrete Dense Network for Long-Sequence Time Series Clustering

arXiv:2405.05015v1 Announce Type: new
Abstract: Time series clustering is fundamental in data analysis for discovering temporal patterns. Despite recent advancements, learning cluster-friendly representations is still challenging, particularly with long and complex time series. Deep temporal clustering methods have been trying to integrate the canonical k-means into end-to-end training of neural networks but fall back on surrogate losses due to the non-differentiability of the hard cluster assignment, yielding sub-optimal solutions. In addition, the autoregressive strategy used in the state-of-the-art RNNs is …

abstract analysis arxiv canonical cluster clustering concrete cs.lg data data analysis fundamental k-means network networks neural networks patterns series temporal time series training type