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

摘要：

时间序列分类是时间序列分析的基础。然而，现有的时间序列分类方法对应的形态特征并不能作为分类依据，且通道间的特征通过图上的单一权重刻画不够准确，导致分类精度不高。因此，提出一种融合衍生特征的时间序列事件分类方法（TSEC-FDF）。首先，在时间序列上构建时间序列事件集合后，根据每个时间序列事件构建突变图、协同图、启发图，以减少噪声对高维特征的干扰；其次，融合多图的特征作为衍生特征，并抽取时间序列事件的多个时间级别的特征；最后，提出一种融合衍生特征的多图卷积分类模型级联时间序列和图特征作为时间序列事件的高维特征。实验结果表明，与TF-C（Time-Frequency Consistency）和BiLSTM+隐马尔可夫模型（Bi-directional Long Short-Term Memory-Hidden Markov Model， BL-HMM）方法相比，TSEC-FDF在4个真实数据集上的准确率、精确率、查全率、F1值、AUROC（Area Under the Receiver Operating Characteristic curve）以及AUPRC（Area Under the Precision versus Recall Curve）至少提升了3.2%、4.7%、7.8%、6.3%、0.9%和2.2%。

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