Research on machine learning forecasting model of economic indicators based on big data

The purpose of this paper is to explore and build a forecasting model of economic indicators, and to improve the accuracy and insight of the future economic trend prediction by integrating advanced machine learning algorithms and massive economic data. In the face of the complexity and variability of the global economic environment and the limitations of traditional forecasting methods, this study first systematically reviewed the classical theories and latest progress in the field of economic forecasting. In the methodology part, this paper compares and analyzes the applicability of various machine learning models (such as support vector machines, gradient lifting decision trees, deep neural networks, etc.) in the prediction of economic indicators, and adopts cross-validation. In particular, according to the characteristics of time series of economic data, recurrent neural network models are introduced to capture long-term dependencies and cyclical patterns in time series. The machine learning forecasting model based on big data shows significant advantages in predicting major economic indicators (such as GDP growth rate, unemployment rate, inflation rate, etc.), especially in forecasting accuracy and early warning ability. In addition, this paper also discusses in depth the explanatory nature of the model, the assessment of forecast uncertainty, and the potential economic policy implications, providing a valuable reference for policy makers, market analysts, and researchers. In summary, this study not only contributes to the method innovation in the field of economic forecasting, but also provides an empirical basis and theoretical support to promote the intellectualization of economic management.