Z-Time: efficient and effective interpretable multivariate time series classification
Zed Lee, Tony Lindgren, Panagiotis Papapetrou
Abstract
Abstract Multivariate time series classification has become popular due to its prevalence in many real-world applications. However, most state-of-the-art focuses on improving classification performance, with the best-performing models typically opaque. Interpretable multivariate time series classifiers have been recently introduced, but none can maintain sufficient levels of efficiency and effectiveness together with interpretability. We introduce , a novel algorithm for effective and efficient interpretable multivariate time series classification. employs temporal abstraction and temporal relations of event intervals to create interpretable features across multiple time series dimensions. In our experimental evaluation on the UEA multivariate time series datasets, achieves comparable effectiveness to state-of-the-art non-interpretable multivariate classifiers while being faster than all interpretable multivariate classifiers. We also demonstrate that is more robust to missing values and inter-dimensional orders, compared to its interpretable competitors.