Interpretable Categorization of Heterogeneous Time Series Data

The explanation of heterogeneous multivariate time series data is a central problem in many applications. The problem requires two major data mining challenges to be addressed simultaneously: Learning models that are humaninterpretable and mining of heterogeneous multivariate time series data. The intersection of these two areas is not adequately explored in the existing literature. To address this gap, we propose grammar-based decision trees and an algorithm for learning them. Grammar-based decision tree extends decision trees with a grammar framework. Logical expressions, derived from context-free grammar, are used for branching in place of simple thresholds on attributes. The added expressivity enables support for a wide range of data types while retaining the interpretability of decision trees. By choosing a grammar based on temporal logic, we show that grammar-based decision trees can be used for the interpretable classification of high-dimensional and heterogeneous time series data. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to analyze the classic Australian Sign Language dataset as well as categorize and explain near midair collisions to support the development of a prototype aircraft collision avoidance system.