Research on industrial economic forecasting algorithm based on big data analysis

With the rapid development of big data technology, industrial economic forecasting is gradually shifting from traditional statistical models to modern forecasting algorithms relying on big data analysis. In this paper, we systematically study the application of big data analytics in industrial economic forecasting, firstly introducing the characteristics of big data and its advantages in economic forecasting. Subsequently, we discuss in detail a variety of existing forecasting algorithms, including traditional statistical models, machine learning models and deep learning models, and analyze the performance and limitations of these algorithms in practical applications. This paper focuses on how to improve and optimize existing prediction algorithms through data preprocessing, feature selection and extraction, and model optimization. Through specific case studies, we demonstrate the application effects of the improved prediction algorithms in different industries and provide in-depth analysis of the prediction results. The study shows that the prediction algorithms based on big data analytics have significant advantages in improving the prediction accuracy, real-time performance and application range. The current challenges facing the application of big data in industrial and economic forecasting are analyzed, including data quality issues, algorithm complexity and computational cost, and result interpretability, and the directions and suggestions for future research are proposed. Overall, the research in this paper provides a theoretical foundation and practical guidance for the further application of big data analytics in industrial economic forecasting, which is of great significance for improving the performance and application effect of forecasting models.