Local Explanation Methods for Time Series Classification Models
Việt Hoa Nguyễn, Phuc M. Vu, Lê Thanh Thảo, Bac Hoai Le · VNU Journal of Science Computer Science and Communication Engineering · 2025
This paper focuses on researching and proposing perturbation-based model-agnosticmethods to explain time series classification models. The main objective of this study is to explainthe predictions of the model, or in other words, to give reasons why it classifies a time series intoa particular label in a set of labels. In this work, we aim to provide the reliability of the decisionand the importance of features in the model. Moreover, in real-world time series, variations in thespeed or scale of a particular action can determine the class, so modifying this type of feature leadsto arbitrary explanations of the time series. To achieve the set objectives, we provide two methods,each with its own strategies and advantages: the LIME-based method and the SHAP method, withthe novelty of using them in combination with data perturbation techniques, especially the ones thataffect the above-mentioned characteristics of the time series.