TimeCED: Contribution-Based Evidence Discovery for Multivariate Time Series Classification

Multivariate time series classification (MTSC) models typically integrate temporal and cross-channel information into sequence-level representations, which may obscure important local information. Existing methods explicitly model or emphasize local regions, but do not directly quantify how each region contributes to the classification objective. Motivated by this, we propose TimeCED, a Contribution-Based Evidence Discovery framework that separates sequence-level representation learning from local evidence discovery. TimeCED directly quantifies the contribution of each channel-temporal region through the change in classification loss induced by candidate removal. Rather than computing this effect exactly, we use a first-order Taylor expansion to efficiently approximate it. High-contribution candidates are consolidated and reused in subsequent training to improve classification performance. We evaluate TimeCED on all 30 datasets in the UEA multivariate time series classification archive, where it achieves strong performance against nine baseline methods. Further analyses confirm the effectiveness of the defined contribution score and the reliability of the Taylor approximation.