  Unsupervised learning of time series data, also known as temporal clustering,
is a challenging problem in machine learning. Here we propose a novel
algorithm, Deep Temporal Clustering (DTC), to naturally integrate
dimensionality reduction and temporal clustering into a single end-to-end
learning framework, fully unsupervised. The algorithm utilizes an autoencoder
for temporal dimensionality reduction and a novel temporal clustering layer for
cluster assignment. Then it jointly optimizes the clustering objective and the
dimensionality reduction objec tive. Based on requirement and application, the
temporal clustering layer can be customized with any temporal similarity
metric. Several similarity metrics and state-of-the-art algorithms are
considered and compared. To gain insight into temporal features that the
network has learned for its clustering, we apply a visualization method that
generates a region of interest heatmap for the time series. The viability of
the algorithm is demonstrated using time series data from diverse domains,
ranging from earthquakes to spacecraft sensor data. In each case, we show that
the proposed algorithm outperforms traditional methods. The superior
performance is attributed to the fully integrated temporal dimensionality
reduction and clustering criterion.
