Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

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
View PDF8 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • 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

Examples

Experimental program
Comparison scheme
Effect test

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...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

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 ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06F17/30
Inventor 刘博贺玺
Owner BEIJING UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com