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DOI	10.1007/s11053-018-9442-z
	Improving Prediction Accuracy of Rainfall Time Series By Hybrid SARIMA-GARCH Modeling
	Pandey, P. K.; Tripura, H.; Pandey, V.
通讯作者	Pandey, V.
来源期刊	NATURAL RESOURCES RESEARCH
ISSN	1520-7439
EISSN	1573-8981
出版年	2019
卷号	28 期号:3 页码:1125-1138
英文摘要	In this paper, a hybrid of seasonal autoregressive integrated moving average (SARIMA)-generalized autoregressive conditional heteroscedasticity (GARCH) was applied to eliminate conditional variance of the SARIMA model of rainfall time series in two different climatic environments (Agartala: humid, and Jodhpur: arid). In addition, the effectiveness of data normalization techniques (differencing and transformation) to stabilize conditional variance in the SARIMA residuals is additionally examined. The residuals from SARIMA models were tested for heteroscedasticity, utilizing the McLeod-Li test, and demonstrated some autocorrelation. Then, the rainfall time series was transformed (differencing and Box-Cox) so that the effect of heteroscedasticity is eliminated. The hybrid SARIMA-GARCH model based on transformed rainfall time series resulted in good statistics performance indices at both climatic environments. The findings of the study suggest that the performance of SARIMA models can be enhanced by using appropriate transformation (Box-Cox) along with GARCH model of residuals of highly skewed rainfall time series from both climatic environments. For Agartala station of monthly rainfall time series, the best model was SARIMA (0, 1, 1) (0, 1, 1)(12)-GARCH (1, 2) with coefficient of determination (R-2)=0.72 and root-mean-square error (RMSE)=25.22, but after Box-Cox transformation of data, the best model was SARIMA (0, 1, 1) (0, 1, 1)(12)-GARCH (2, 4) with R-2=0.87 and RMSE=0.672. For the monthly rainfall series of Jodhpur station, the best model was SARIMA (0, 1, 1) (0, 1, 1)(12)-GARCH (1, 2) with R-2=0.68 and RMSE=16.75, but after Box-Cox transformation of data the best model was SARIMA (0, 1, 1) (0, 1, 1)(12)-GARCH (1, 2) with R-2=0.79 and RMSE=1.917. The performance indices indicate that hybrid (SARIMA-GARCH) models fitted to transformed time-series rainfall data performed best in the humid as well as the arid regions.
英文关键词	Nonlinear time series Heteroscedasticity SARIMA model GARCH model Box-Cox transformation Ljung-Box test McLeod-Li test
类型	Article
语种	英语
国家	India
收录类别	SCI-E
WOS记录号	WOS:000467136200028
WOS关键词	FIT
WOS类目	Geosciences, Multidisciplinary
WOS研究方向	Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/217729
作者单位	North Eastern Reg Inst Sci & Technol, Dept Agr Engn, Itanagar 791109, Arunachal Prade, India
推荐引用方式 GB/T 7714	Pandey, P. K.,Tripura, H.,Pandey, V.. Improving Prediction Accuracy of Rainfall Time Series By Hybrid SARIMA-GARCH Modeling[J],2019,28(3):1125-1138.
APA	Pandey, P. K.,Tripura, H.,&Pandey, V..(2019).Improving Prediction Accuracy of Rainfall Time Series By Hybrid SARIMA-GARCH Modeling.NATURAL RESOURCES RESEARCH,28(3),1125-1138.
MLA	Pandey, P. K.,et al."Improving Prediction Accuracy of Rainfall Time Series By Hybrid SARIMA-GARCH Modeling".NATURAL RESOURCES RESEARCH 28.3(2019):1125-1138.