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.

翻译：时间序列聚类是数据分析中发现时序模式的基础任务。尽管近期取得进展，学习聚类友好的表示仍具挑战性，尤其是处理长且复杂的时间序列。深度时间聚类方法试图将经典k-means融入神经网络的端到端训练中，但由于硬聚类分配的非可微性而依赖替代损失函数，导致次优解。此外，最先进的递归神经网络所使用的自回归策略存在误差累积和训练缓慢的问题，而最新研究发现Transformer因时间点缺乏语义含义、注意力的置换不变性会丢弃时间顺序以及计算成本高昂而效果欠佳。基于上述观察，我们提出LoSTer——一种用于长序列时间序列聚类问题的新型稠密自编码器架构，通过Gumbel-softmax重参数化技巧优化k-means目标，并专门设计用于长时间序列的准确快速聚类。在多个基准数据集和两个实际应用上的大量实验证明了LoSTer相较于最先进的递归神经网络和基于Transformer的深度聚类方法的有效性。