TIME SERIES ARIMA MODEL FOR PREDICTING MONTHLY NET RADIATION (FUDMA JOURNAL OF SCIENCES, 2022)
Net radiation is not a climatic variable hence not observed. Tedious numerical computations have been shown to characterize the methods used in its determination using data on some climatic variables. This study aims at generating monthly synthetic net radiation data in Ibadan, Benue and Kano, Nigeria using the Autoregressive Integrated Moving Average (ARIMA) model. This study performed Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analysis in determining the parameters of the model while, the residual plots of Autocorrelation Function (ACF) and Partial Autocorrelation Functions (PACF) and graphical plots of backward model predictions or estimates and their respective actual values were used in the model validation. The study reveals that, the first difference of monthly net radiation can be represented by ARIMA (2, 1, 2) for Ibadan and Kano, and ARIMA (1, 1, 1) for Benue. Further result showed that there is a significant and fairly strong positive correlation between the monthly actual and predicted net radiation values across stations (p < 0.05). Lastly, the residual plots of Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) for Benue, Ibadan and Kano were examined and it was observed that the residuals were within the confidence intervals. This affirms the fact that the Autoregressive Integrated Moving Average (ARIMA) model is of good fit.
Keywords: ARIMA model; Autocorrelation Function; Net radiation; Partial Autocorrelation Function