基于异构特征融合的多维时间序列分类算法

Multivariate Time Series Classification Algorithm Based on Heterogeneous Feature Fusion

随着大数据时代的到来和传感器的发展,多维时间序列分类问题成为数据挖掘领域的重要问题。多维时间序列存在维度高、维度间关系复杂、数据形态多变的特点,从而生成巨大的特征空间。现有方法难以选取有区分力的特征,导致方法的准确度普遍较低。另一方面,现有方法的分类结果的可解释性较差。针对上述问题,提出了一种基于异构特征融合的多维时间序列分类算法。该算法融合了时域、频域和区间统计值这3种特征并对特征进行聚类,从而找到最有代表性的特征。首先为每个维度提取不同类型的代表性特征,再通过多维度特征转换的方法融合所有维度的不同类型的特征,形成特征向量,并基于此训练分类模型。为了提高分类结果的可解释性,算法基于树结构生成不同类型的候选特征集合,然后通过聚合消除冗余和相似的特征,最终获得少量代表性特征。为了验证所提算法的有效性,在公开的UEA数据集上进行了大量实验。实验结果显示,所提算法的准确性、特征融合的合理性,以及分类结果的可解释性均优于现有方法。

With the advance of big data and sensors,multivariable time series classification has been an important problem in data mining.Multivariate time series are characterized by high dimensionality,complex inter-dimensional relations,and variable data forms,which makes the classification methods generate huge feature spaces,and it is difficult to select discriminative features,resulting in low accuracy and hindering the interpretability.Therefore,a multivariate time series classification algorithm based on heterogeneous feature fusion is proposed in this paper.The proposed algorithm integrates time-domain,frequency-domain,and interval-based features.Firstly,a small number of representative features of different types are extracted for each dimension.Then,features of all dimensions are fused by multivariable feature transformation to learn the classifier.For univariate feature extraction,the algorithm generates different types of feature candidates based on tree structure,and then a clustering algorithm is designed to aggregate redundant and similar features to obtain a small number of representative features,which effectively reduces the number of features and enhances the interpretation of the method.In order to verify the effectiveness of the algorithm,expensive experiments are conducted on the public UEA dataset,and the proposed algorithm is compared with the existing multivariate time series classification methods.The results prove that the proposed algorithm is more accurate than the comparison methods,and the feature fusion is reasonable.What’s more,the interpretability of classification results is showed by case study.