Skewed Time Series Classification Algorithm Based on Persistent Homology
To address the limitations of traditional time series classification algorithms to extract high-dimensional topological information and temporal sequence information,this paper proposes a skewed time series classification algorithm based on persistent homology for extracting high-dimensional topological and temporal sequential information.The algorithm combines the variance of temporal data to embed univariate time series data into a two-dimensional point cloud,displaying temporal changes within and between cycles.It performs a time skew on the subintervals divided by the sliding window and decomposes the point cloud into different structures through time skew.Thus,the algorithm can adapt to more temporal data and effectively capture temporal order information.It uses persistent homology technology to construct Vietoris-Rips(VR)complex flows on point clouds,analyzes the changes in number of holes in various dimensions at different scales to extract more comprehensive topological structural features of the temporal data,and obtains topological features in point clouds by calculating persistence diagram.It uses persistent center representation vectors as input and classifies point clouds using a random forest model.The results of comparative experiments performed on nine UCR time series datasets show that the algorithm achieves the highest accuracy on eight of these datasets.Compared to six traditional time series classification algorithms,the algorithm achieves classification accuracy improvement and increase in F1 value by 0.5-24 percentage points and 0.9-23.9 percentage points,respectively.This indicates its higher accuracy and good robustness in time series data classification.
time seriestime series classificationtime skewpersistent homologypersistence diagram
万方数据
维普
