Improving Multivariate Time Series Classification with Class-Wise Training and Model Aggregation

• URL: http://arxiv.org/abs/2609.07493v1
• Date: Mon, 07 Sep 2026 13:41:33 GMT
• Title: Improving Multivariate Time Series Classification with Class-Wise Training and Model Aggregation
• Abstract summary: We propose a class-wise dimension (channel) selection framework for Multivariate Time Series Classification (MTSC)<n>Rather than applying a single global dimension selection process, the proposed approach independently identifies informative dimensions for each class.<n>The objective is to improve the generation of discriminative feature representations while reducing the influence of noisy or non-informative dimensions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a class-wise dimension (channel) selection framework for Multivariate Time Series Classification (MTSC). Rather than applying a single global dimension selection process, the proposed approach independently identifies informative dimensions for each class. A dedicated learning process is subsequently performed for each class, followed by a fusion stage for final prediction. The objective is to improve the generation of discriminative feature representations while reducing the influence of noisy or non-informative dimensions. The proposed framework is evaluated using MiniRocket, a random kernel-based baseline method. Experimental results indicate that class-wise dimension selection improves the quality of extracted representations and can enhance classification performance, particularly in high-dimensional settings. These findings suggest that incorporating class-specific information into the training process represents a promising direction for MTSC, improving robustness through consistent gains across heterogeneous datasets, and interpretability through the explicit identification of class-relevant dimensions.

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