Interpretable Clustering of Multivariate Time Series with Time2Feat

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Abstract

This paper showcases Time2Feat, an end-to-end machine learning system for Multivariate Time Series (MTS) clustering. The system relies on interpretable inter-signal and intra-signal features extracted from the time series. Then, a dimensionality reduction technique is applied to select a subset of features that retain most of the information, thus enhancing the interpretability of the results. In addition, the system enables domain specialists to semi-supervise the process by submitting a small collection of MTS with a target cluster. This process further improves both accuracy and interpretability, by reducing the number of features used by the clustering process. The demonstration shows the application of Time2Feat to various MTS datasets, by creating clusters from MTS datasets of interest, experimenting with different settings and using the approach capabilities to interpret the clusters generated.