关键词: 转换图, 衍生特征, 图卷积神经网络, 多图融合, 时间序列分类, 图构建

Abstract:

Time series classification is the foundation of time series analysis. However， the morphological features corresponding to the existing time series classification methods cannot serve as the basis for classification， and the features between channels are not characterized accurately by the single weight on the graph， resulting in low classification accuracy. Therefore， a Time Series Event Classification method Fused with Derived Features （TSEC-FDF） was proposed. Firstly， after constructing a time series event set on the time series， the mutation graphs， collaborative graphs， and heuristic graphs were constructed on the basis of each time series event to reduce noise interference to high-dimensional features. Secondly， the features of multiple graphs were fused and treated as derived features， and features of time series events at multiple time levels were extracted. Finally， a multi-graph convolutional classification model fusing derived features was proposed， where time series and graph features were cascaded as high-dimensional features of time series events. Experimental results show that TSEC-FDF improves the accuracy， precision， recall， F1 score， AUROC（Area Under the Receiver Operating Characteristic） curve， and AUPRC（Area Under the Precision versus Recall Curve） on 4 real datasets by 3.2%， 4.7%， 7.8%， 6.3%， 0.9%， and 2.2%， at least， compared to TF-C （Time-Frequency Consistency） and Bi-directional Long Short-Term Memory-Hidden Markov Model （BL-HMM） methods.

Key words: transfer graph, derived feature, graph convolutional neural network, multi-graph fusion, time series classification, graph construction