Time series classification using forests of randomized shapelet trees
Shapelets have recently been proposed for data series classification, due to their ability to capture phase independent and local information. Decision trees based on shapelets have been shown to provide not only interpretable models, but also, in many cases, state-of-the-art predictive performance. Shapelet discovery is, however, computationally costly, and although several techniques for speeding up this task have been proposed, the computational cost is still in many cases prohibitive. In this talk, I will discuss an ensemble-based method, referred to as Random Shapelet Forest (RSF), that has been recently proposed, which builds on the success of the random forest algorithm, and which is shown to have a lower computational complexity than the original shapelet tree learning algorithm. An extensive empirical investigation shows that the algorithm provides competitive predictive performance and that a proposed way of calculating importance scores can be used to successfully identify influential regions.