COSCO: A Sharpness-Aware Training Framework for Few-shot Multivariate Time Series Classification
<|reference_start|>COSCO: A Sharpness-Aware Training Framework for Few-shot Multivariate Time Series Classification: 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.<|reference_end|>
@article{barreda2024cosco:,
    title={COSCO: A Sharpness-Aware Training Framework for Few-shot Multivariate
  Time Series Classification},
    author={Jesus Barreda, Ashley Gomez, Ruben Puga, Kaixiong Zhou, Li Zhang},
    journal={arXiv preprint arXiv:2409.09645},
    year={2024},
    doi={10.1145/3627673.3679891},
    archivePrefix={arXiv},
    eprint={2409.09645},
    primaryClass={cs.LG cs.AI cs.NE}
}