In view of the increasingly complex economic environment and the rising demand for fiscal management, as an important tool for the country to implement macroeconomic regulation and formulate fiscal policies, tax revenue forecasting, its accuracy and stability are of great significance to fiscal management. Traditional statistical methods have certain deficiencies in modeling and data adaptability, and a single model is difficult to deal with the nonlinearity, volatility and uncertainty of tax data. This paper is based on the concept of integrating intelligent algorithms and statistical learning, and builds a tax revenue forecasting framework based on a combination model. It uses integrated time series models (such as ARIMA), machine learning algorithms (such as XGBoost ) and intelligent optimization techniques (such as genetic algorithms) to achieve the mutual complementation of multiple model advantages and improve prediction accuracy and robust performance. The experiment is based on many years of tax data from a certain region in China. From multiple evaluation indicators, the model is better than the traditional method, which verifies the effectiveness and feasibility of the method. The research expands the technical path of tax forecasting in the scope of methodology and also presents a data-driven decision support magic weapon for fiscal management departments.

Xingyu Yang. Research on Tax Revenue Forecasting Based on Combined Model under the Fusion of Intelligent Algorithm and Statistical Learning. Socio-Economic Statistics Research (2025), Vol. 6, Issue 1: 43-53. https://doi.org/10.38007/SESR.2025.060105.

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