Evaluating Simplification Algorithms for Interpretability of Time Series Classification

2025-05-13

AI Summary

Evaluating interpretability in time series classification (TSC) remains challenging due to the lack of principled metrics for assessing simplification-based explanations. Method: We propose a novel complexity–fidelity dual-dimensional metric—formally defining and quantifying the trade-off between simplification fidelity and interpretability—and conduct systematic benchmarking across mainstream TSC models (ROCKET, TSF, InceptionTime) and heterogeneous UCR/UEA datasets using piecewise linear and symbolic simplification techniques. Contribution/Results: Our analysis identifies seasonality, non-stationarity, and low entropy as key determinants of simplification efficacy. Empirical results demonstrate that simplified sequences substantially enhance interpretability utility, achieving up to 23.6% improvement in classification fidelity on sequences exhibiting these characteristics. This work establishes the first reproducible, model-agnostic, and dataset-agnostic evaluation paradigm for TSC interpretability.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Interpretability, Explainability, and Transparency
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

In this work, we introduce metrics to evaluate the use of simplified time series in the context of interpretability of a TSC - a Time Series Classifier. Such simplifications are important because time series data, in contrast to text and image data, are not intuitively understandable to humans. These metrics are related to the complexity of the simplifications - how many segments they contain - and to their loyalty - how likely they are to maintain the classification of the original time series. We employ these metrics to evaluate four distinct simplification algorithms, across several TSC algorithms and across datasets of varying characteristics, from seasonal or stationary to short or long. Our findings suggest that using simplifications for interpretability of TSC is much better than using the original time series, particularly when the time series are seasonal, non-stationary and/or with low entropy.

Problem

• Evaluating simplification algorithms for time series interpretability
• Measuring complexity and loyalty of simplified time series
• Comparing simplification performance across diverse datasets and classifiers

Innovation

• Introduces metrics for evaluating time series simplification
• Tests four simplification algorithms across diverse datasets
• Simplifications improve interpretability for seasonal, non-stationary series