  Time series clustering is a challenging task due to the specific nature of
the data. Classical approaches do not perform well and need to be adapted
either through a new distance measure or a data transformation. In this paper
we investigate the combination of a convolutional autoencoder and a k-medoids
algorithm to perfom time series clustering. The convolutional autoencoder
allows to extract meaningful features and reduce the dimension of the data,
leading to an improvement of the subsequent clustering. Using simulation and
energy related data to validate the approach, experimental results show that
the clustering is robust to outliers thus leading to finer clusters than with
standard methods.
