Abstract: 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.

Key words: Multivariate time series, Time series classification, Feature fusion, Interpretability, Feature clustering