Optimal shapelets tree for time series interpretable classification

Time series shapelets are a state-of-the-art data mining technique that is applied to time series supervised classification tasks. Shapelets are defined as subsequences that retain the most discriminating power contained in time series. The main advantage of shapelets-based methods consists of their great interpretability. Indeed, shapelets can provide the end-user with very helpful insights about the most interesting subsequences. In this paper, we propose a novel Mixed-Integer Programming model to optimize shapelets discovery based on optimal binary decision trees. Our formulation provides a flexible and adaptable classification framework that is interpretable with respect to both the mathematical model and the final output. Computational results for a large class of datasets show that our approach achieves performance comparable with state-of-the-art shapelets-based classification methods. Our model is the first approach based on optimal decision tree induction for time series classification.