Batch Training for Streaming Time Series: A Transferable Augmentation Framework to Combat Distribution Shifts
1、We revise the title to better highlight the specific content of the methodology.
2、We add the experimental conclusions to the abstract.
3、In Section 1, We re-induct and summarize the contributions of the methodology.
4、In Section 2, we incorporate a more comprehensive literature review.
5、In Section 3, we add the problem definition of distribution shift and corrected typos.
6、In Section 4, we add more advanced baselines, re-run the experiments based on more reasonable experimental settings, and present specific cases of distribution shift along with visualization results demonstrating the superiority of the methodology.
7、In Appendix A.3, we add the experimental standard deviations of the methodology.
8、In Appendix B.3, we add more comprehensive and complete experimental result tables.