Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals

TL;DR

This paper introduces a convolutional neural network with an attention mechanism that identifies important variables and time intervals in multivariate time series, enhancing explainability and outperforming existing methods.

Contribution

The authors propose a novel modular convolution-based model with attention for explainable multivariate time series classification, highlighting relevant variables and time intervals.

Findings

Outperforms state-of-the-art baseline methods on benchmark datasets.

Identified variables and time intervals align with domain knowledge.

Model provides interpretable insights into classification decisions.

Abstract

Time series data is prevalent in a wide variety of real-world applications and it calls for trustworthy and explainable models for people to understand and fully trust decisions made by AI solutions. We consider the problem of building explainable classifiers from multi-variate time series data. A key criterion to understand such predictive models involves elucidating and quantifying the contribution of time varying input variables to the classification. Hence, we introduce a novel, modular, convolution-based feature extraction and attention mechanism that simultaneously identifies the variables as well as time intervals which determine the classifier output. We present results of extensive experiments with several benchmark data sets that show that the proposed method outperforms the state-of-the-art baseline methods on multi-variate time series classification task. The results of our case studies demonstrate that the variables and time intervals identified by the proposed method make sense relative to available domain knowledge.