  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.
