Prediction Algorithm of Digital Economy Development Trend Based on Big Data

Abstract

With the development of economic globalization, people have higher requirements for economic analysis and prediction, while the prediction accuracy of the traditional economic analysis and prediction model is quite low. Therefore, with the adaptation of the times, the “big data and artificial intelligence” technology is presented for researchers and data analysts. Based on the “big data and artificial intelligence” technology, economic analysis and prediction have unique advantages in many aspects. This study analyses the impact of “big data and artificial intelligence” technology on economic analysis and prediction. Based on the mean square error criterion, the variable selection method for constructing the interval data model is given. The interval financial time-series data of stock market, fund market, futures market, and money market are used to predict and analyze the macroeconomy, and the macroeconomic interval prediction method is given, which is different from the traditional point data model. The empirical results show that the Shenzhen Component Index, Shanghai fund index, futures market transaction amount, and narrow money supply in the interval financial data have little fitting error to the macroeconomic interval prediction model. Through the first mock examination, we get the macroeconomic forecasting model based on interval financial time-series data. We also use the single model structure and the combined model structure to give the macroeconomic change interval of 2020–2023 years in China. The experimental research results show that the proposed model has good performance in the prediction of economic development trends and that it can be used for forecasting subsequent economic development forecasts.