提出流式批量PCA方法，高效处理长时序多变量数据分类。
Temporal Streaming Batch Principal Component Analysis for Time Series Classification
- 用流式分块PCA动态压缩时序数据，持续更新紧凑表示。
- 序列越长效果越好，最长数据集准确率提升7.2%，耗时降49.5%。
- 适合处理长序列多变量数据的模型优化，尤其在资源受限场景。
在多变量时间序列分类中，现有序列分析模型虽具备良好分类能力，但在处理长序列多变量数据时存在训练时间长、准确率下降等显著缺陷。本文针对长序列多变量数据优化模型性能，缓解长时序和多变量对模型的影响。提出一种基于主成分分析（PCA）的时序流式批量压缩与降维算法（时序流式批量PCA，TSBPCA），通过时间块更新的流式PCA时间估计，持续更新整个序列的紧凑表示，增强多种序列分析模型的数据表征能力。我们在五个真实数据集上使用多种模型验证该方法，实验结果表明，该方法在分类准确率和时间效率方面表现优异。尤其随着序列长度增加，效果愈发显著：在两个最长序列数据集上，准确率提升约7.2%，执行时间减少49.5%。
原文摘要 · Abstract (English)
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. We evaluated this method using various models on five real datasets, and the experimental results show that our method performs well in terms of classification accuracy and time efficiency. 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%, and execution time decreased by 49.5%.
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