Dataset Condensation for Time Series Classification via Dual Domain
  Matching
Zhanyu Liu Ke Hao Guanjie Zheng Yanwei Yu
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.