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Time series clustering is a challenging task due to the specific nature ofthe data. Classical approaches do not perform well and need to be adaptedeither through a new distance measure or a data transformation. In this paperwe investigate the combination of a convolutional autoencoder and a k-medoidsalgorithm to perfom time series clustering. The convolutional autoencoderallows to extract meaningful features and reduce the dimension of the data,leading to an improvement of the subsequent clustering. Using simulation andenergy related data to validate the approach, experimental results show thatthe clustering is robust to outliers thus leading to finer clusters than withstandard methods.

Autoencoder-based time series clustering with energy applications