  Unsupervised clustering of temporal data is both challenging and crucial in
machine learning. In this paper, we show that neither traditional clustering
methods, time series specific or even deep learning-based alternatives
generalise well when both varying sampling rates and high dimensionality are
present in the input data. We propose a novel approach to temporal clustering,
in which we (1) transform the input time series into a distance-based projected
representation by using similarity measures suitable for dealing with temporal
data,(2) feed these projections into a multi-layer CNN-GRU autoencoder to
generate meaningful domain-aware latent representations, which ultimately (3)
allow for a natural separation of clusters beneficial for most important
traditional clustering algorithms. We evaluate our approach on time series
datasets from various domains and show that it not only outperforms existing
methods in all cases, by up to 32%, but is also robust and incurs negligible
computation overheads.
