Matrix Profile-Based Interpretable Time Series Classifier - Guidotti, Riccardo; D’Onofrio, Matteo (Frontiers in Artificial Intelligence, 2021)
Time series classification is a pervasive and transversal problem in various fields ranging from disease diagnosis to anomaly detection in finance. Unfortunately, the most effective models used by Artificial Intelligence systems for time series classification are not interpretable and hide the logic of the decision process, making them unusable in sensitive domains. Recent research is focusing on explanation methods to pair with the obscure classifier to recover this weakness. However, a time series classification approach that is transparent by design, and that is simultaneously efficient and effective is even more preferable. To this aim, we propose an interpretable time series classification method based on the patterns that is possible to extract from the Matrix Profile of the time series in the training set. A smart design of the classification procedure allows obtaining an efficient and effective transparent classifier modeled as a decision tree that expresses the reasons for the classification as the presence of discriminative subsequences. Quantitative and qualitative experimentation shows that the proposed method overcomes state-of-the-art interpretable approaches.