Temporal Streaming Batch Principal Component Analysis for Time Series Classification (Student Abstract)
Enshuo Yan, Huachuan Wang, Weihao Xia
Abstract
In multivariate time series classification, although current sequence analysis models have excellent classification capabilities, they show significant shortcomings when dealing with long sequence multivariate data. This paper focuses on optimizing model performance for long-sequence multivariate data by mitigating the impact of extended time series and multiple variables on the model. We propose a principal component analysis (PCA)-based temporal streaming compression and dimensionality reduction algorithm for time series data (temporal streaming batch PCA, TSBPCA), which continuously updates the compact representation of the entire sequence through streaming PCA time estimation with time block updates, enhancing the data representation capability of a range of sequence analysis models.We evaluated this method using various models on five datasets, and the experimental results show that our method demonstrates outstanding performance in both classification accuracy and time efficiency.
BibTeX
@article{Yan_Wang_Xia_2025, title={Temporal Streaming Batch Principal Component Analysis for Time Series Classification (Student Abstract)}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35319}, DOI={10.1609/aaai.v39i28.35319}, abstractNote={In multivariate time series classification, although current sequence analysis models have excellent classification capabilities, they show significant shortcomings when dealing with long sequence multivariate data. This paper focuses on optimizing model performance for long-sequence multivariate data by mitigating the impact of extended time series and multiple variables on the model. We propose a principal component analysis (PCA)-based temporal streaming compression and dimensionality reduction algorithm for time series data (temporal streaming batch PCA, TSBPCA), which continuously updates the compact representation of the entire sequence through streaming PCA time estimation with time block updates, enhancing the data representation capability of a range of sequence analysis models.We evaluated this method using various models on five datasets, and the experimental results show that our method demonstrates outstanding performance in both classification accuracy and time efficiency.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Yan, Enshuo and Wang, Huachuan and Xia, Weihao}, year={2025}, month={Apr.}, pages={29543-29544} }