A method for mining Granger causality between visibility multidimensional spatio-temporal data

A causal relationship, multi-dimensional space-time technology, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as lack of perfect solutions, and achieve the effect of avoiding combinatorial explosion problems

Active Publication Date: 2018-12-18
BEIJING UNIV OF TECH
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However, these methods are mainly used to discover the static correlation of single variables, which have great limitations in practical applications. There is

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  • A method for mining Granger causality between visibility multidimensional spatio-temporal data
  • A method for mining Granger causality between visibility multidimensional spatio-temporal data
  • A method for mining Granger causality between visibility multidimensional spatio-temporal data

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[0034] The present invention will be further described in detail below in conjunction with specific examples and with reference to the drawings.

[0035] The hardware equipment used in the present invention has a PC machine;

[0036] Such as figure 2 As shown, the present invention provides a method for mining Granger causality between visibility multidimensional spatiotemporal data, which specifically includes the following steps:

[0037] Step 1. Obtain a multi-dimensional space-time series data set in the field of atmospheric visibility, and preprocess these data.

[0038] Step 2. For different visibility influencing factors, take part of the sample data and use Granger causality analysis to obtain Granger causality among them, and remove the influencing factors that have no Granger causality with visibility.

[0039] Step 2.1, in order to ensure the consistency of the distribution of the selected sample data, the data is divided by stratified sampling and multiple random division m...

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Abstract

The invention discloses a method for mining Granger causality between visibility multi-dimensional spatio-temporal data, belonging to the technical field of data mining. Firstly, the Granger causalityanalysis is used to extract the causal features of visibility from some sample data, and then classify all the data according to different administrative districts of Beijing Municipality, Fc causality measurement factor is used to determine the strength of the influence relationship between different regions, Finally, an improved spatio-temporal Granger Lasso algorithm is used to train the causality model, so that the Granger causality scores of different regions, different influence factors and visibility can be obtained, and the qualitative and quantitative analysis of influence factors can be realized.

Description

technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to mining qualitative and quantitative Granger causality between features from multi-dimensional time-space sequence data. Background technique [0002] A multidimensional time series contains a set of ordered observations at discrete times, which can be viewed as a collection of multiple univariate time series. This kind of sequence data is ubiquitous in traffic forecasting, air conditions, economics, etc. For example, in the field of atmospheric visibility research, in recent years, with the rapid application of fossil fuels, the number of aerosol particles produced by the combustion of oil, coal and waste in the atmosphere has increased significantly, resulting in reduced atmospheric visibility and cloudy air, so visibility pollution The issue has received a lot of attention. Analyzing the influence factors of visibility on different regions and different types ...

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Application Information

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IPC IPC(8): G06F17/30
Inventor 刘博贺玺
Owner BEIJING UNIV OF TECH
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