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Time series trend feature extraction method based on important point double evaluation factor

A time series, evaluation factor technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as reducing noise interference

Active Publication Date: 2018-11-13
CENT SOUTH UNIV
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Problems solved by technology

The method of the invention overcomes the shortcomings of single evaluation function and locality of the existing piecewise linearization method, can effectively weaken noise interference, accurately describe the overall trend, and realize accurate extraction of time series change trend information

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  • Time series trend feature extraction method based on important point double evaluation factor
  • Time series trend feature extraction method based on important point double evaluation factor
  • Time series trend feature extraction method based on important point double evaluation factor

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Embodiment Construction

[0071] Embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0072] figure 1 Is the overall flow chart of the inventive method, as figure 1 Shown, the present invention is based on the trend extraction method of important point double evaluation factor and comprises the following steps:

[0073] Step S01: Initialization, given the initial time series and determining the number of segments, setting the distance factor threshold and weight;

[0074] Step S02: According to the definition of time series important points, select important points as the candidate set of time series segmentation points;

[0075] Step S03: Calculate the distance factor of important points, and use the distance factor to measure the relative difference of important points;...

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Abstract

The invention discloses a time series trend feature extraction method based on an important point double evaluation factor, comprising: regarding a time series piecewise linear representation as basis, defining an important point as an alternative set of time series segmentation points, calculating an important point distance factor and a trend factor, using the distance factor to measure the relative degree of difference and using the trend factor to measure its impact on the overall trend globally, using a comprehensive evaluation model to evaluate the importance of each important point to the overall trend to select the segmentation points, and finally connecting adjacent segmentation points to obtain a segmented trend representation of the time series. The invention proposes the concept of a time series important point distance factor, and combines the two evaluation factors to evaluate the important points of the time series, which overcomes the shortcomings of single evaluation functions and locality of the existing piecewise linearization method, may effectively weaken the noise interference, retain the time series change trend feature, and has fast processing speed and higher extraction precision than the existing method when the number of segments is the same.

Description

technical field [0001] The present invention relates to time series data processing technology, in particular to a method for extracting time series trend features based on important point dual evaluation factors. Background technique [0002] Time series data mining is a hot research topic in the field of data mining in recent years. The results of time series mining are of great significance for dynamic system knowledge acquisition and control decision-making, and have broad application prospects in business, industry, science and other fields. Time series trend change information is an important feature of time series, which can make people understand the information contained in time series more intuitively. Since the time series in practical applications have the characteristics of "high-dimensional and massive", directly applying traditional data processing technology on the original data is not very suitable for the actual needs. [0003] In order to mine the trend ...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N3/00
CPCG06N3/006
Inventor 徐德刚谢婷玉罗聪苏志芳阳春华桂卫华谢永芳
Owner CENT SOUTH UNIV
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