Temporal Streaming Batch Principal Component Analysis for Time Series Classification

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, such as prolonged training times and decreased accuracy. 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. Notably, our method demonstrates a trend of increasing effectiveness as sequence length grows; on the two longest sequence datasets, accuracy improved by about 7.2%, RNNs have the problem of vanishing or exploding gradients As practical demand continues to grow, the scope of time when dealing with long sequences. To overcome these issues, series data has expanded rapidly in terms of quantity and gated mechanisms were introduced, such as gated recurrent the increasing complexity of inherent characteristics [1], such units (GRU) [8] and long short-term memory (LSTM) [9].The as its multivariate structure and extended time spans.