A Zero-delay Nonlinear Extended Granger Causality Analysis Method

Abstract

The invention relates to a non-linear extended Granger causality analysis method with zero time lag, which belongs to the technical field of data mining. The traditional Granger causal model is based on the VAR model of the lagged term, but ignoring the zero time lag will greatly change the model coefficients of the lagged term, leading to wrong causal identification. Secondly, the traditional Granger causal model can only be applied to the causal identification of linear systems, and may produce wrong causal identifications for nonlinear systems. Based on the above analysis, the present invention first expands the traditional VAR model, sets the lag order of the variables, uses the Gaussian kernel function to nonlinearly map the original data, and then establishes a structural VAR model containing zero lag items. Finally, according to the structure Granger causality identification is carried out on the residuals of the VAR model to realize the zero-delay causality analysis of nonlinear systems such as pollution and meteorology. The invention can overcome the deficiency of the traditional Granger causality model, and realize the extended Granger causality analysis of the nonlinear system.

Description

technical field [0001] The invention belongs to the technical field of data mining, and relates to a zero-lag non-linear extended Granger causal analysis method, aiming at mining the (zero) time-lag causal influence relationship among time series variables in nonlinear systems such as meteorology and environment. Background technique [0002] Multivariate time series refers to a collection of a series of digital sequences arranged in chronological order, which widely exist in many fields such as industry, medicine, finance and meteorology. For example, the field of air pollution research. In the past few decades, due to the rapid development of industrialization and the popularization of automobiles in my country, urban air is often polluted by coal combustion, industrial waste gas, waste and other emissions and automobile exhaust, resulting in serious air pollution. Especially in many parts of the north and east of our country, there are different degrees of smog weather. ...

AI Technical Summary

Problems solved by technology

Furthermore, traditional GC models are based on linear VAR models, which may identify spurious causality in the face of nonlinear systems

However, the actual system often has a complex nonlinear structure, so when the traditional GC model analyzes the causality of the actual system, it often faces the risk of wrong causality identification

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