Time sequence classification method and time sequence classification system

Abstract

The invention discloses a time sequence classification method which comprises the following steps of optimizing a GDTW kernel function in advance, and obtaining an improved GDTW kernel function; by means of the improved GDTW kernel function, performing kernel transformation on a preset time sequence training sample set and a time sequence testing sample; and performing classification on kernel transformation data of the testing sample by means of a preset classification algorithm and a time sequence kind label in the kernel transformation data of the training sample, thereby obtaining kind of the time sequence testing sample. According to the time sequence classification method, in calculating an Euclidean distance between two time sequence elements by the improved GDTW kernel function, the Euclidean distance between two time sequence elements which satisfy an optimal offset path is calculated so that the improved GDTW kernel function keeps the offset path information between the time sequences, thereby further improving a subsequent classification effect. Additionally, the invention correspondingly discloses a time sequence classification system.

Description

technical field [0001] The invention relates to the technical field of time series classification, in particular to a time series classification method and system. Background technique [0002] Time series is an ordered sequence of various values ​​of a certain phenomenon or statistical index at different time points, arranged in chronological order. The classification of time series has always been the focus of researchers in the field of time series data mining. With the advent of the era of big data, it is particularly important to quickly and efficiently classify messy time series. Currently, classification algorithms for classifying time series mainly include nearest neighbor classifiers, support vector machines, and sparse coding algorithms. [0003] At present, when using the nearest neighbor classifier, support vector machine or sparse coding algorithm to classify time series, it is necessary to perform kernel transformation processing on the time series in advance...

AI Technical Summary

Problems solved by technology

However, because the GDTW kernel function only uses DTW as a distance measure, it simply replaces the Euclidean distance in the Gaussian kernel function, and ignores the more important offset path information in DTW, so the classification effect of the time series is not good enough. needs further improvement

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