Classification method based on kernel feature extraction early prediction multivariate time series category

Abstract

The invention provides a classification method based on a kernel feature extraction early prediction multivariate time series category according to early prediction multivariate time series classification. To extract the essential features of variable time series, first the variable time series undergo feature extraction respectively, and a clustering method is adopted to reduce redundancy features, remove noise and improve classification stability; then, to improve classification efficiency, precision and early degree, a method for comprehensively evaluating feature performances is provided on the basis of accuracy rate, recall rate and the early degree and the like, and the optimal feature in each cluster is selected to serve as a kernel feature of a variable; and finally, two simple effective classifier construction methods are provided on the basis of a kernel feature set of each variable. Correctness and effectiveness of the method and an algorithm are proven through experiments, and experiment results prove that a classifier can have high accuracy rate and good early degree.

Description

technical field [0001] The invention relates to the technical field of time series data mining, in particular to a classification method for early prediction of multivariate time series categories based on kernel features. Background technique [0002] In recent years, in the field of time series mining, the classification problem in multivariate time series data mining has become a hot spot, and it is widely used in multimedia, medicine, manufacturing industry, financial applications and other application fields. For the classification of multivariate time series, domestic and foreign scholars have proposed a variety of methods to construct multivariate time series classifiers. [0003] In order to improve the accuracy of classification, Iyad Batal and other scholars based on extracting the abstract features of multivariate time series, converted multivariate time series data into Boolean vectors, and finally used traditional machine learning methods for classification. Sc...

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Problems solved by technology

[0005] The above literature is mainly aimed at the classification of multivariate time series, and does not discuss the prediction of its category in advance.

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