COSCO: A Sharpness-Aware Training Framework for Few-shot Multivariate Time Series Classification
This addresses the challenge of limited labeled data for practitioners in domains relying on multivariate time series classification, though it is incremental as it builds on existing optimization and loss techniques.
The paper tackles the problem of poor generalization in few-shot multivariate time series classification by proposing COSCO, a framework combining sharpness-aware minimization and a prototypical loss function, which outperforms existing baseline methods in experiments.
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.