Dataset Condensation for Time Series Classification via Dual Domain Matching
<|reference_start|>Dataset Condensation for Time Series Classification via Dual Domain Matching: Time series data has been demonstrated to be crucial in various research fields. The management of large quantities of time series data presents challenges in terms of deep learning tasks, particularly for training a deep neural network. Recently, a technique named \textit{Dataset Condensation} has emerged as a solution to this problem. This technique generates a smaller synthetic dataset that has comparable performance to the full real dataset in downstream tasks such as classification. However, previous methods are primarily designed for image and graph datasets, and directly adapting them to the time series dataset leads to suboptimal performance due to their inability to effectively leverage the rich information inherent in time series data, particularly in the frequency domain. In this paper, we propose a novel framework named Dataset \textit{\textbf{Cond}}ensation for \textit{\textbf{T}}ime \textit{\textbf{S}}eries \textit{\textbf{C}}lassification via Dual Domain Matching (\textbf{CondTSC}) which focuses on the time series classification dataset condensation task. Different from previous methods, our proposed framework aims to generate a condensed dataset that matches the surrogate objectives in both the time and frequency domains. Specifically, CondTSC incorporates multi-view data augmentation, dual domain training, and dual surrogate objectives to enhance the dataset condensation process in the time and frequency domains. Through extensive experiments, we demonstrate the effectiveness of our proposed framework, which outperforms other baselines and learns a condensed synthetic dataset that exhibits desirable characteristics such as conforming to the distribution of the original data.<|reference_end|>
@article{liu2024dataset,
    title={Dataset Condensation for Time Series Classification via Dual Domain
  Matching},
    author={Zhanyu Liu, Ke Hao, Guanjie Zheng, Yanwei Yu},
    journal={arXiv preprint arXiv:2403.07245},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07245},
    primaryClass={cs.LG}
}