Time series classification method and device

Abstract

The invention discloses a time series classification method and device. The method comprises the following steps of: aiming at a to-be-classified test time series, determining a first dynamic time regular distance between the test time series and each training time series in a pre-obtained training time series set; aiming at each determined first dynamic time regular distance, determining a first Gaussian function on the basis of the first dynamic time regular distance, and obtaining a Gaussian kernel transformation matrix of the test time series; obtaining a sparse representation coefficient matrix of the test time series according to the Gaussian kernel transformation matrix and a dictionary matrix of the pre-generated training time series set; aiming at each type, determining a residual error between the test time series and the type according to an atom, corresponding to a position of the type, in the dictionary matrix and the sparse representation coefficient matrix; and determining the type corresponding to the minimum residual error as a type of the test time series. By applying the time series classification method and device provided by embodiments of the invention, the time series classification accuracy can be improved.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to a time series classification method and device. Background technique [0002] Time series refers to an orderly sequence in which the values ​​of a certain phenomenon or statistical index at different time points are arranged in chronological order. The classification of time series has always been the focus of researchers in the field of time series data mining technology. [0003] Currently, time series are often classified according to a Gaussian kernel function based on Euclidean distance. Since Euclidean distance does not have good adaptability to time series, it is difficult for the Gaussian kernel function based on Euclidean distance to provide a better dictionary for time series classification, making the accuracy of time series classification low. Contents of the invention [0004] The purpose of the present invention is to provide a time series classification met...

AI Technical Summary

Problems solved by technology

Since Euclidean distance does not have good adaptability to time series, it is difficult for the Gaussian kernel function based on Euclidean distance to provide a better dictionary for time series classification, making the accuracy of time series classification low

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