Regional Economic Development and Urbanization Trend Prediction Based on Extemporization Data

In the prediction of regional economic development and urbanization process, accurate trend positioning is very important. However, existing digital mining algorithms often lead to performance problems due to inaccurate location. These algorithms can't effectively solve the imprecise positioning of regional economy and urbanization trend, and their results are usually unsatisfactory. Therefore, a prediction method based on immediate data is proposed to improve this situation and it is evaluated. First, the influencing factors are identified using dataset theory, and the indicators are classified according to the demand of trend forecasting to reduce interference. Then, a real-time data-driven economic and urbanization trend prediction model is constructed by using data set theory, and its prediction results are analyzed in detail. The MATLAB simulation results show that this real-time data method performs better in prediction accuracy and time of economic impact variables compared with traditional digital mining algorithms under specific evaluation conditions.