Stochastic characteristics and modeling of minimum and maximum temperature of Ogun State, Nigeria

Abstract

Previous 20 years data (1982 to 2009) have been collected in order to predict the future temperature pattern of Ogun State. The data were preprocessed and aggregated into annual time series to fit for stochastic characterization and modeling of minimum and maximum. Mann-Kendal non-parametric test, Lo’s long-range dependency test and spectral analysis were done to detect whether there is trend and seasonal component in the time series The best autoregressive AR-model, moving average MA-model and autoregressive moving average ARMA-models were fitted for all parameters considered, with the aid of Akaike Information Criterion (AIC), and error terms of FE, MAE, MSE and MAPE. AR, MA and ARMA models of order (2), (3) and (1, 2) and (5),

(3) and (5, 3) were found to be the best for predicting maximum and minimum temperatures respectively.ACF, PACF and the Box-Jenkins technique were utilized for model type and order selection. The overall results were promising and the prediction scheme applied in this research could be considered in situations where database is a problem during model development. It is therefore recommended that another research be carry out in the area using another method of modeling to compare the results.