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.
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页码：103 / 107
页数：5
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