A Method for Mining Granger Causality Between Visibility Multidimensional Spatial-Temporal Data

A causal relationship, multi-dimensional space-time technology, applied in the direction of electrical digital data processing, digital data information retrieval, special data processing applications, etc., can solve problems such as lack of perfect solutions, and achieve the effect of strong model interpretability and wide application fields

Active Publication Date: 2021-07-30
BEIJING UNIV OF TECH
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Problems solved by technology

However, these methods are mainly used to discover the static correlation of single variables, which have great limitations in practical applications. There is no problem in mining the qualitative and quantitative causal relationship between massive time series data and multidimensional data in space. perfect solution

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  • A Method for Mining Granger Causality Between Visibility Multidimensional Spatial-Temporal Data
  • A Method for Mining Granger Causality Between Visibility Multidimensional Spatial-Temporal Data
  • A Method for Mining Granger Causality Between Visibility Multidimensional Spatial-Temporal Data

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Embodiment Construction

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

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

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

[0037] Step 1. Obtain the multidimensional spatio-temporal series data set in the field of atmospheric visibility, and preprocess the data.

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

[0039] Step 2.1, in order to ensure the distribution consistency of the selected partial sample data, use stratified sampling and mu...

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Abstract

The invention discloses a method for digging the Granger causality between visibility multi-dimensional spatio-temporal data, which belongs to the technical field of data mining. First select part of the sample data and use Granger causality analysis to extract the candidate features that have a causal relationship with visibility, then classify all the data according to different administrative regions of Beijing, and use the Fc causal measurement factor 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 between different regions, different influencing factors and visibility can be obtained, and qualitative and quantitative influencing factor analysis 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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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/2458
Inventor 刘博贺玺
Owner BEIJING UNIV OF TECH
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