Methodology and Application of Fiscal and Tax Forecasting Analysis Based on Multi-Source Big Data Fusion

Abstract

With the advent of the big data era, the use of computers has spread to all walks of life, and the finance and taxation industry is also in the middle of it. The current taxation system is huge and complex, and different tax types are inevitably linked to different economic indicators at a deep level, so tax forecasting requires personalised forecasting analysis for different tax types. This paper selects several tax types that account for a large proportion of tax revenue for prediction analysis, respectively, and conducts fusion research on multi-source big data, including business tax, corporate income tax, and personal income tax. Based on the multi-source big data fusion method, the prediction research on fiscal taxation tax types is conducted, and experiments are conducted with the taxation data of Beijing from 1995 to 2020 to predict the three tax types from 2017 to 2020. The results show that the deviation of the forecast data from the real tax data is small, controlling the forecast deviation to within 14%, indicating the effectiveness of the proposed method.