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Time sequence data classification method based on data feature fragments

A technology of data characteristics and time series, applied in database models, relational databases, electrical digital data processing, etc., can solve problems such as long training time, and achieve the effect of improving screening efficiency

Pending Publication Date: 2019-07-16
SHANDONG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

In addition, the ST method also has corresponding defects: the training time of this method is relatively long, which will undoubtedly have a great impact on the execution efficiency of the classification operation.

Method used

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  • Time sequence data classification method based on data feature fragments
  • Time sequence data classification method based on data feature fragments
  • Time sequence data classification method based on data feature fragments

Examples

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Embodiment

[0062]A method for classifying time series data based on data feature segments. This embodiment takes the world-recognized time series standard data set as an example (http: / / www.timeseriesclassification.com / ) to describe the corresponding implementation. The present invention selects the "Symbols" standard data set for detailed description of the processing steps. The training set of the "Symbols" data has 25 time series, each sequence length is 398, and the 25 sequences are divided into 6 categories. Such as figure 1 shown, including the following steps:

[0063] S1, preset data trend point selection rate ρ, selection quantity threshold of data feature segment Shapelets Num=1 / 2Num(D), data feature segment Shapelets quality evaluation standard is information gain (information gain), subclass division standard rate The corresponding subclass division standard rate μ is used in subsequent steps to complete the corresponding subclass division operation.

[0064] The selection...

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Abstract

The invention relates to a time sequence data classification method based on data feature fragments. The method comprises the following steps of firstly, carrying out subclass division on all sequences in different classes in a specific time sequence training set; meanwhile, selecting a corresponding central sequence; for the center sequence of each subclass, reusing the data trend points to select the data feature fragments of larger weights preferentially according to the weight sorting of the data features to thereby form a data feature fragment set Shapets, and finally finishing the finaltime sequence classification by using a corresponding Shapets conversion algorithm by taking the Shapets set as a core. According to the method, the efficiency of Shapetes generation is greatly improved, and meanwhile, the overall processing efficiency of the algorithm for classifying the time sequence based on the Shapetes is also improved, and the efficiency improvement amplitude is more than three orders of magnitudes.

Description

technical field [0001] The invention relates to a time series data classification method based on data feature segments, and belongs to the technical field of "massive" and "high-dimensional" time series data mining. Background technique [0002] At present, with the rapid development of technologies such as the Internet, the Internet of Things, and cloud computing, relevant technical means and corresponding implementations have been widely used in government affairs, finance, transportation, and medical care. While promoting the rapid development of the information society, these technical means have also produced massive amounts of information data, that is, we have gradually entered the era of "big data". Among the massive information data, there is a type of data that uses "time" as a reference, which can not only reflect the specific data characteristics at a certain time, but also reflect the basic change trend of data within a certain period of time, thus further reve...

Claims

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Application Information

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IPC IPC(8): G06F16/2458G06F16/28
CPCG06F16/2474G06F16/285
Inventor 罗伟胡宇鹏李学庆徐鹏涛丁一明
Owner SHANDONG UNIV
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