COSCO: A Sharpness-Aware Training Framework for Few-shot Multivariate Time Series Classification

• URL: http://arxiv.org/abs/2409.09645v1
• Date: Sun, 15 Sep 2024 07:41:55 GMT
• Title: COSCO: A Sharpness-Aware Training Framework for Few-shot Multivariate Time Series Classification
• Authors: Jesus Barreda, Ashley Gomez, Ruben Puga, Kaixiong Zhou, Li Zhang,
• Abstract summary: We propose a new learning framework named COSCO consisting of a sharpness-aware minimization (SAM) optimization and a Prototypical loss function. Our experiments demonstrate our proposed method outperforms the existing baseline methods.
• Score: 19.593625378366472
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Multivariate time series classification is an important task with widespread domains of applications. Recently, deep neural networks (DNN) have achieved state-of-the-art performance in time series classification. However, they often require large expert-labeled training datasets which can be infeasible in practice. In few-shot settings, i.e. only a limited number of samples per class are available in training data, DNNs show a significant drop in testing accuracy and poor generalization ability. In this paper, we propose to address these problems from an optimization and a loss function perspective. Specifically, we propose a new learning framework named COSCO consisting of a sharpness-aware minimization (SAM) optimization and a Prototypical loss function to improve the generalization ability of DNN for multivariate time series classification problems under few-shot setting. Our experiments demonstrate our proposed method outperforms the existing baseline methods. Our source code is available at: https://github.com/JRB9/COSCO.

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