Time series data classification method based on multi-level shape

Abstract

The invention relates to a time series data classification method based on multi-level shape. The method comprises the following steps: 1) preprocessing time series data: carrying out data dimension reduction processing on an original time series by using an SAX method; 2) obtaining an initial sub-sequence of the time sequence: extracting a sub-sequence set in the time sequence by a window slidingmethod, and indirectly controlling the extraction length of the sub-sequence by changing and adjusting the size of a window; step 3) discovery and extraction of a multi-level Shapelet candidate set:filtering and merging the candidate set through the proposed multi-level Shapelet framework, and selecting the Shapelet with large information gain as the candidate set; and (4) carrying out Shapeletconversion and constructing a classifier. According to the invention, the method is adopted, an efficient multi-level shapelet candidate set filtering model is provided, the number of Shapelet candidate sets is effectively reduced, the Shapelet sets with high classification capacity are rapidly screened, and then effective classification of time series data is achieved through an ELM classifier.

Description

technical field [0001] The invention belongs to the field of time series data mining, relates to a classification method of time series data, and the invention specifically relates to a time series data classification method based on multi-level shapelets all the time. Background technique [0002] Time series data usually represent the results obtained by observing a certain potential process according to the set sampling frequency within an equal interval of time. The data comes from medical diagnosis, disaster prediction, commercial monitoring and other fields. Time series data generally has the characteristics of large data volume, high dimensionality, and fast update. Time series data classification has always been a key issue in the field of time series data mining and has a wide range of applications. In time series classification tasks, shapelet technology is an effective method to solve time series classification problems. The shapelet-based classification method ...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the shortcomings of the existing shapelet classification methods for time series, the present invention provides a time series data classification method based on multi-level shapelets, which can quickly and effectively deal with the problem of accurate classification of high-dimensional time series data

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