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

TL;DR

EvoTSC introduces a genetic programming method that automatically evolves lightweight, generalizable feature learning models for time series classification, effectively incorporating prior knowledge and reducing overfitting.

Contribution

The paper presents a novel evolutionary approach that embeds expert knowledge and employs a Pareto tournament to improve model generalization in time series classification.

Findings

EvoTSC outperforms eleven benchmark methods on univariate datasets.

The evolved models are resource-efficient and highly generalizable.

Component analysis confirms the effectiveness of the design choices.

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.