Using Kriging Technique to Interpolate and Forecasting Temperatures Spatio-Temporal Data (European Journal of Pure and Applied Mathematics, 2023)
This paper deals with the forecasting temperatures’ spatio-temporal data. This research aims to examine the performance of two statistical methods for interpolating and predicting Spatio-temporal. The kriging technique and a dynamic semi-parameter factor model are applied in this work. The data adopted in this work represent the temperature in Mosul city and Baghdad city in Iraq. The results of our findings show the behavior prediction is closed to the fitting model based on the cross-validation through the comparison between the kriging method and dynamic semi-parametric factor model, we are getting that kriging prediction is more efficient with the second method of the dynamic model. In conclusion, the results show that prediction is consistent with geographic basis risk, also the performance of the dynamic semi-parameter factor model appears to the extent of geographic basis risk to describe the information of the prediction model.
Keywords: ordinary kriging; semi parametric model; geographic basis risk; spatiotemporal data