Detecting human influence on climate using neural networks based Granger causality

被引：22

作者：

Attanasio, A. ^{[1
]}

Triacca, U. ^{[1
]}

机构：

[1] Univ Aquila, I-67100 Laquila, Italy

来源：

THEORETICAL AND APPLIED CLIMATOLOGY | 2011年 / 103卷 / 1-2期

关键词：

TIME-SERIES; MODEL; PREDICTION;

D O I：

10.1007/s00704-010-0285-8

中图分类号：

P4 [大气科学（气象学）];

学科分类号：

0706 ; 070601 ;

摘要：

In this note we observe that a problem of linear approach to Granger causality testing between CO2 and global temperature is that such tests can have low power. The probability to reject the null hypothesis of non-causality when it is false is low. Regarding non-linear Granger causality, based on multi-layer feed-forward neural network, the analysis provides evidence of significant unidirectional Granger causality from CO2 to global temperature.

引用

页码：103 / 107

页数：5

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