EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming

2026-04-28

AI Summary

Time series classification is often hindered by scarce labeled data, high computational costs, and overfitting. To address these challenges, this work proposes EvoTSC—a lightweight feature learning framework based on genetic programming. EvoTSC incorporates expert prior knowledge into a multi-layer program structure to guide model evolution and introduces a Pareto tournament selection strategy to enhance generalization and stability across data subsets. Evaluated on multiple univariate time series classification benchmarks, the method significantly outperforms eleven state-of-the-art approaches, demonstrating both the effectiveness and efficiency of its key components.

Abstract

Time series classification is an important analytical task across diverse domains. However, its practical application is often hindered by the scarcity of labeled data and the requirement for substantial computational resources. To address these challenges, this paper proposes EvoTSC, a novel genetic programming approach designed to automatically evolve lightweight feature learning models for time series classification. The core of EvoTSC is a carefully designed multi-layer program structure that strategically embeds diverse forms of prior expert knowledge into the evolutionary process, effectively guiding the search toward operations known to be highly effective for time series analysis. To mitigate the common overfitting problem in time series classification, a tailored Pareto tournament selection strategy is proposed to favor models that perform consistently well across varying training data subsets, promoting the discovery of highly generalizable models. Extensive experiments conducted on univariate time series classification datasets demonstrate that EvoTSC significantly outperforms eleven benchmark methods in most comparisons. Further analyses verify the contribution of each component and the resource efficiency of the evolved models.

Problem

• time series classification
• labeled data scarcity
• computational resources
• overfitting
• model generalization

Innovation

• Genetic Programming
• Time Series Classification
• Feature Learning
• Pareto Tournament Selection
• Lightweight Models