Complexity measures and features for times series classification

Abstract

Time series classification is a growing problem in different disciplines due to the progressive digitalization of the world. The best state-of-the-art algorithms focus on performance, seeking the best possible results, leaving interpretability at a second level, if any. Furthermore, interpretable proposals are far from providing competitive results. In this work, focused on time series classification, we propose a new representation of time series based on a robust and complete set of features. This new representation allows extracting more meaningful information on the underlying time series structure to develop effective classifiers whose results are much easier to interpret than current state-of-the-art models. The proposed feature set allows using the traditional vector-based classification algorithms in time series problems, significantly increasing the number of techniques available for this type of problem. To evaluate the performance of our proposal, we have used the state-of-the-art repository of time series classification, UCR, composed of 112 datasets. The experimental results show that through this representation, more interpretable classifiers can be obtained which are competitive. More specifically, they obtain no statistically significant differences from the second and third-best models of the state-of-the-art. Apart from competitive results in accuracy, our proposal is able to improve the model interpretability based on the set of features proposed.