Journal of Pharmacognosy and Phytochemistry
Vol. 6, Issue 5 (2017)
Time series analysis model to forecast rainfall for Allahabad region
Anosh Graham and Ekta Pathak Mishra
The prediction of Rainfall on monthly and seasonal time scales is not only scientifically Challenging but is also important for planning and devising agricultural strategies. Various research groups attempted to predict rainfall on a seasonal time scales using different techniques. This paper describes the Box-Jenkins time series seasonal ARIMA (Auto Regression Integrated Moving Average) approach for prediction of rainfall on monthly scales. Seasonal ARIMA model (0, 0, 0) (0, 1, 0)
for rainfall was identified the best model to forecast rainfall for next 5 yearâ€™s with confidence level of 95 percent by analyzing last 31 yearâ€™s data (1985-20015). Previous years data is used to formulate the seasonal ARIMA model and in determination of model parameters. The performance evaluations of the adopted models are carried out on the basis of correlation coefficient (R2
) and root mean square error (RMSE).The study conducted at Allahabad, Uttar Pradesh (India). The results indicate that the seasonal ARIMA model provide consistent and satisfactory predictions for rainfall parameters on monthly scale.
Pages: 1418-1421 | 885 Views 234 Downloads
Anosh Graham and Ekta Pathak Mishra. Time series analysis model to forecast rainfall for Allahabad region. J Pharmacogn Phytochem 2017;6(5):1418-1421.