Time Series Domain Adaptation via Sparse Associative Structure Alignment


  • Ruichu Cai Guangdong University of Technology
  • Jiawei Chen Guangdong University of Technology
  • Zijian Li Guangdong University of Technology
  • Wei Chen Guangdong University of Technology
  • Keli Zhang Huawei Noah’s Ark Lab
  • Junjian Ye Huawei Noah's Ark Lab
  • Zhuozhang Li Guangdong University of Technology
  • Xiaoyan Yang Guangdong University of Technology
  • Zhenjie Zhang Guangdong University of Technology




Transfer/Adaptation/Multi-task/Meta/Automated Learning


Domain adaptation on time series data is an important but challenging task. Most of the existing works in this area are based on the learning of the domain-invariant representation of the data with the help of restrictions like MMD. However, such extraction of the domain-invariant representation is a non-trivial task for time series data, due to the complex dependence among the timestamps. In detail, in the fully dependent time series, a small change of the time lags or the offsets may lead to difficulty in the domain invariant extraction. Fortunately, the stability of the causality inspired us to explore the domain invariant structure of the data. To reduce the difficulty in the discovery of causal structure, we relax it to the sparse associative structure and propose a novel sparse associative structure alignment model for domain adaptation. First, we generate the segment set to exclude the obstacle of offsets. Second, the intra-variables and inter-variables sparse attention mechanisms are devised to extract associative structure time-series data with considering time lags. Finally, the associative structure alignment is used to guide the transfer of knowledge from the source domain to the target one. Experimental studies not only verify the good performance of our methods on three real-world datasets but also provide some insightful discoveries on the transferred knowledge.




How to Cite

Cai, R., Chen, J., Li, Z., Chen, W., Zhang, K., Ye, J., Li, Z., Yang, X., & Zhang, Z. (2021). Time Series Domain Adaptation via Sparse Associative Structure Alignment. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 6859-6867. https://doi.org/10.1609/aaai.v35i8.16846



AAAI Technical Track on Machine Learning I