Time Series Analysis of Nyala Rainfall Using ARIMA Method

Abstract

This paper presents linear stochastic models known as multiplicative seasonal autoregressive integrated moving average model (SARIMA).The model is used to simulate monthly rainfall in Nyala station, Sudan. For the analysis, monthly rainfall data for the years 1971–2010 were used. The seasonality observed in Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) plots of monthly rainfall data was removed using first order seasonal differencing prior to the development of the SARIMA model. Interestingly, the SARIMA (0,0,0)x(0,1,1)12 model developed was found to be most suitable for simulating monthly rainfall over Nyala station. This model is considered appropriate to forecast the monthly rainfall to assist decision makers to establish priorities for water demand, storage, distribution and disaster management.