International Journal of Mathematics Trends and Technology
Volume 38 | Number 1 | Year 2016 | Article Id. IJMTT-V38P504 | DOI : https://doi.org/10.14445/22315373/IJMTT-V38P504
Rainfall is of critical importance for many people particularly those whose livelihoods are dependent on rain fed agriculture. Predicting the trend of rainfall is a difficult task in meteorology and environmental sciences. Statistical approaches from time series analysis provide an alternative way for predicting the patterns of rainfall. This paper describes an empirical study of modeling and forecasting seasonal rainfall patterns in India. The Box-Jenkins SARIMA Methodology has been adopted for forecasting, the diagnostic checking has shown that the seasonal model 4 (0,0,0) (0,1,1) fitted to the series is appropriate, and forecast are obtained on the basis of the fitted model. Seasonal ARIMA model was a proper method for modeling and predicting the time series of seasonal rainfall patterns.
[1] Stoffer, D.S. and R.H. Dhumway, Time Series Analysis and its Application. 3rd Edn, Springer, New York, ISBN-10: 1441978658, pp: 596, 2010.
[2] Cryer, J. D. and K.S. Chan, Time Series Analysis with Application in R. 2nd Edn., Springer, New York, ISBN-10: 0387759581,2008, pp: 491.
[3] Kantz, H. and T. Schreiber. Nonlinear Time Series Analysis. 2nd Edn, Cambridge University Press, Cambridge, ISBN-10: 0521529026, 2004, pp: 369.
[4] He S.Y., 2004, Applied time series analysis 1st Edn., Peking University press, Beijing.
[5] Eni, D. and Adeyeye, F.J. (2015) Seasonal ARIMA Modeling and Forecasting of Rainfall in Warri Town, Nigeria. Journal of Geoscience and Environment Protection, 3, 91-98. http://dx.doi.org/10.4236/gep.2015.36015
[6] Metrine Chonge, Kennedy Nyongesa , Omukoba Mulati, Lucy Makokha, Frankline Tireito (2015) A Time Series Model of Rainfall Pattern of Uasin Gishu County, IOSR Journal of Mathematics, Volume 11, Issue 5, pp. 77-84
[7] H. R. Wang1, C. Wang1, X. Lin2, and J. Kang2 An improved ARIMA model for precipitation simulations, Nonlin. Processes Geophys., 21, 1159–1168, 2014
[8] Wang, J., Y.H. Du and X.T. Zhang, Theory and Application with Seasonal Time Series. 1st Edn, Nankai University Press, Chinese, 2008.
[9] Guo, Z.W., The adjustment method and research progress based on the ARIMA model. Chinese J. Hosp. Stat., 161:2009, 65-69.
[10] Chartfield,C, “Inverse Autocorrelations” Journal of Royal Statistical Society, A142,1980, 363-377.
[11] Brocklebank,J.C. Dickey D.A., SAS System for Forecasting Time Series 2nd edition, Cary North Carolina: SAS Institute Inc., 2003 [12] Weesakul, U. and Lowanichchai, S. “Rainfall Forecast for Agricultural Water Allocation Planning in Thailand” Thammasat International Journal of Science and Technology, 10(3), 2005, pp. 18-27.
[13] Xinghua Chang, Meng Gao, Yan Wang and Xiyong Hou, Seasonal Autoregressive Integrated Moving Average Model for Precipitation Time Series. Journal of Mathematics and Statistics 8(4): 2012, pp. 500-505.
[14] Momani, M. and P.E. Naill, Time series analysis model for rainfall data in Jordan: Case study for using time series analysis. Am. J. Environ. Sci., 5: 599-604. 2009, DOI: 10.3844/ajessp.2009.599.604
Subbaiah Naidu K.CH.V, "SARIMA Modeling and Forecasting of Seasonal Rainfall Patterns in India," International Journal of Mathematics Trends and Technology (IJMTT), vol. 38, no. 1, pp. 15-22, 2016. Crossref, https://doi.org/10.14445/22315373/IJMTT-V38P504
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