Unlocking Domain Adaptation for Time-Series Analysis through Temporal Mixup

In this article, we present a new method called Sparse Associative Structure Alignment (SASA) for time series classification. The approach leverages the idea of associative structure discovery and aligns the sparse structures between the source and target domains. The authors propose a novel algorithm that combines an adaptive summarization of subsequences, a Maximum Mean Discrepancy (MMD)-based structure alignment method, and a single layer LSTM for feature extraction.
The article begins by introducing the problem of time series classification and the limitations of traditional methods, which are often limited to simple statistical models or hand-crafted features. The authors then introduce SASA as a novel approach that aligns the sparse associative structure between the source and target domains, enabling the transfer of knowledge from the source domain to the target domain.
The proposed algorithm consists of three main steps: (1) extracting features from the original time series using a single layer LSTM; (2) adaptively summarizing the subsequences of the original time series; and (3) aligning the sparse structures between the source and target domains using an MMD-based method. The authors demonstrate the effectiveness of SASA on several benchmark datasets, showcasing its superior performance compared to other state-of-the-art methods.
The article also provides a detailed analysis of the results, including a comprehensive evaluation of the average performances across different datasets and classifiers. The authors observe that SASA achieves the lowest average rank amongst all classifiers, which they attribute to various factors, such as the model’s original design for detecting events in a time series rather than classifying the whole time series as input.
Overall, the article provides a valuable contribution to the field of time series classification by proposing a novel approach that leverages the associative structure discovery and alignment method to transfer knowledge from the source domain to the target domain. The proposed algorithm demonstrates superior performance compared to other state-of-the-art methods, making it an attractive choice for practitioners and researchers working in this area.