In short, so many other works bothering on this subject is almost surely in literature. For example: Okorie and Akpanta 2015 modelled extreme monthly rainfall in Ikeja, Nigeria using the Generalized Pareto distribution Etuk, et al., 2013 identified and established the adequacy of a Seasonal ARIMA (5,1,0)(0,1,1) 12 for modelling and forecasting the amount of monthly rainfall in Portharcourt, Nigeria Edwin and Martins 2014 examined the stochastic characteristics of the Ilorin monthly rainfall in Nigeria using four different modelling techniques (Decomposition, Square root transformation-deseasonalisation, Composite and Periodic Autoregressive) where they compared the results from the various methods employed. Introduction Hitherto, a lot of attention has been directed towards modelling and forecasting the amount of rainfall in various parts of Nigeria. Comparison of the actual/observed frequency from July to December 2011 was done with their corresponding forecast values and a t-test of significance showed no significant difference.ġ. Though the diagnostic check on the model favoured the fitted model, the Auto Regressive parameter was found to be statistically insignificant and this led to a reduced SARIMA (0, 0, 0) (0, 1, 1) 12 model that best fit the data and was used to make forecast. The plots of the ACF and PACF show spikes at seasonal lags respectively, suggesting SARIMA (0,0,0) (1,1,1) 12. The graph further displays evidence of seasonality and it was removed by seasonal differencing.
The Plot of the original data shows that the time series is stationary and the Augmented Dickey-Fuller test did not suggest otherwise.
The analysis was based on probability time series modelling approach. This work considered the frequency of monthly rainfall from 1996 to 2011 obtained from National Root Crops Research Institute Umudike in Nigeria.
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