Concrete Dense Network for Long-Sequence Time Series Clustering

• URL: http://arxiv.org/abs/2405.05015v1
• Date: Wed, 8 May 2024 12:31:35 GMT
• Title: Concrete Dense Network for Long-Sequence Time Series Clustering
• Authors: Redemptor Jr Laceda Taloma, Patrizio Pisani, Danilo Comminiello,
• Abstract summary: Time series clustering is fundamental in data analysis for discovering temporal patterns. Deep temporal clustering methods have been trying to integrate the canonical k-means into end-to-end training of neural networks. LoSTer is a novel dense autoencoder architecture for the long-sequence time series clustering problem.
• Score: 4.307648859471193
• License: http://creativecommons.org/licenses/by/4.0/
• 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 subject to error accumulation and slow training, while recent research findings have revealed that Transformers are less effective due to time points lacking semantic meaning, to the permutation invariance of attention that discards the chronological order and high computation cost. In light of these observations, we present LoSTer which is a novel dense autoencoder architecture for the long-sequence time series clustering problem (LSTC) capable of optimizing the k-means objective via the Gumbel-softmax reparameterization trick and designed specifically for accurate and fast clustering of long time series. Extensive experiments on numerous benchmark datasets and two real-world applications prove the effectiveness of LoSTer over state-of-the-art RNNs and Transformer-based deep clustering methods.

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