Abstract:
In this paper, the results of seasonal modeling and subsequent forecasting
of Sokoto monthly average temperature have been obtained using seasonal
autoregressive integrated moving average modeling approach. Based on this seasonal
modeling analysis, we conclude that the SARIMA(l,0,0)(0,1,1)12 ,SARIMA
(3,0,1)(4,1,0)12 and SARlMA (4,0,2)(5,1,1)12 models are adequate for a good
description of temperature in Sokoto. We further asses their forecastibility from the
out-of-sample forecast statistic, Results show that for the short forecast statistics
SARlMA (3,0,1)(4, I ,0) 12 model minimizes the mean squares error of the forecast,
while the middle forecast and long forecast statistics results have shown that
SARlMA (4,0,2)(5, I, I) 12 model has optimal forecast to the Sokoto temperature,
hence this models have the advantage of capturing and describing and forecasting
the seasonal dynamics of Sokoto city temperature.
