Classification method based on kernel feature extraction early prediction multivariate time series category
A technology of time series and kernel features, applied in character and pattern recognition, instruments, calculations, etc., can solve problems such as uncategorized discussions, and achieve the effects of reducing redundant features, improving stability, and high accuracy
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[0032] The technical solution of the present invention will be described in detail below in conjunction with the drawings and embodiments.
[0033] The invention proposes an effective method for extracting kernel features of the multivariate time series aiming at the classification problem of the early prediction multivariate time series. Through the extraction and selection of kernel features for each variable time series of multivariate time series, and then using the kernel feature set of each variable, a classifier is constructed through two simple and effective classification methods.
[0034] The embodiment of the present invention takes the Wafer data set as a specific example. The Wafer data contains 2 categories (respectively marked as abnormal category and normal category), and each data includes 6 variables, that is, each data includes a time series of 6 variables. The training data set contains 192 data, and the test data set contains 48 data. In order to weaken t...
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