Classification trees for time series

This paper proposes an extension of classification trees to time series input variables. A new split criterion based on time series proximities is introduced. First, the criterion relies on an adaptive (i.e., parameterized) time series metric to cover both behaviors and values proximities. The metrics parameters may change from one internal node to another to achieve the best bisection of the set of time series. Second, the criterion involves the automatic extraction of the most discriminating subsequences. The proposed time series classification tree is applied to a wide range of datasets: public and new, real and synthetic, univariate and multivariate data. We show, through the experiments performed in this study, that the proposed tree outperforms temporal trees using standard time series distances and performs well compared to other competitive time series classifiers.

► This paper proposes an extension of the classification trees to time series input variables.
► The proposed split criterion is based on an adaptive time series metric to cover both behavior and values proximities.
► An automatic extraction is performed to extract the most discriminating sub-sequences.
► The carried out experiments show that the proposed tree outperforms temporal trees using standard time series distances.
► The proposed tree leads to good performances compared to other competitive time series classifiers.