Adaptive Segmentation of Multivariate Time Series with FastICA and G-G Clustering

WANG Ling; LI Ze-zhong

doi:10.12263/DZXB.20220649

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Adaptive Segmentation of Multivariate Time Series with FastICA and G-G Clustering

PAPERS | 更新时间：2025-12-08

- Adaptive Segmentation of Multivariate Time Series with FastICA and G-G Clustering
- ACTA ELECTRONICA SINICA Vol. 51, Issue 5, Pages: 1235-1244(2023)
- 作者机构：
  
  1.北京科技大学自动化学院，北京100083
  2.北京科技大学自动化学院工业过程知识自动化教育部重点实验室，北京100083
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20220649
  CLC： TP273
- Received：07 June 2022，
  
  Revised：2022-08-22，
  
  Published：25 May 2023
- 稿件说明：
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王玲,李泽中.基于FastICA和G-G聚类的多元时序自适应分段[J].电子学报,2023,51(05):1235-1244.

WANG Ling,LI Ze-zhong.Adaptive Segmentation of Multivariate Time Series with FastICA and G-G Clustering[J].ACTA ELECTRONICA SINICA,2023,51(05):1235-1244.
王玲,李泽中.基于FastICA和G-G聚类的多元时序自适应分段[J].电子学报,2023,51(05):1235-1244. DOI： 10.12263/DZXB.20220649.

WANG Ling,LI Ze-zhong.Adaptive Segmentation of Multivariate Time Series with FastICA and G-G Clustering[J].ACTA ELECTRONICA SINICA,2023,51(05):1235-1244. DOI： 10.12263/DZXB.20220649.

摘要

现有多元时间序列的分段方法主要通过检测时序数据统计特性或形状的变化情况，并以此为依据对分段点的位置进行“硬划分”.然而，这些分段方法无法对两个分段之间的过渡区间长度进行准确估计，且普遍需要人为预先设置参数，在高维且噪声较强的情况下分段效果较差.本文针对现有分段方法存在的诸多不足，提出一种基于FastICA（Fast Independent Component Analysis）和G-G（Gath-Geva）模糊聚类的多元时序自适应分段方法.该方法利用FastICA进行特征提取，采用DW（Durbin-Watson）指数自动选取高信噪比的主成分，并根据最小描述长度（Minimum Description Length，MDL）设计基于G-G模糊聚类的自适应分段模型，实现对于多元时间序列的“软划分”.基于多种领域的真实数据集实验结果表明：与现有主流的分段方法相比，本文方法在上述数据集上的平均

和MAE（Mean Absolute Error）可分别提升8.4%~16.8%和3.06%~6.56%.

Abstract

The existing segmentation methods detect the statistical or shape changes of multivariate time series

and perform crisp segmentation on the location of change points. However

these methods fail to estimate the length of the transition interval between two segments

cannot accurately segment multivariate time series with high dimension

strong noise

and need to set parameters in advance. To address such matters

an adaptive multivariate time series segmentation method based on FastICA (Fast Independent Component Analysis) and G-G (Gath-Geva) clustering is proposed. In this method

the key features of multivariate time series are extracted via FastICA

and DW (Durbin-Watson) criterion is used to automatically select main components with high signal-to-noise ratio. According to the minimum description length (MDL)

an adaptive multivariate time series segmentation model based on G-G clustering is designed

which is able to perform soft segmentation of multivariate time series. The experimental analysis is carried out on real datasets in many different fields. Compared with state-of-art benchmarks

the average

and MAE (Mean Absolute Error) of the proposed method on the above-mentioned datasets improve 8.4%~16.8% and 3.06%~6.56%

respectively.

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Keywords

references

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