Spatial Combination Forecasting Model Based on Panel Data and Its Empirical Study

In order to improve the accuracy of spatial forecasting based on panel data, the significance of spatial autocorrelation on the panel data is tested by Moran I, the first-order spatial autoregressive model and the Kriging algorithm model are established from the perspective of the cross-sectional data, respectively, and back-propagation neural network model trained by the genetic algorithm is established from the perspective of the time-series data. Then a spatial combination forecasting model based on panel data is established by the three single models. The weights are obtained by the information entropy approach. An empirical study shows that the spatial combination forecasting model is the most effective in the accuracy and robustness.