Learning Model for Analytical Prediction of Tax Revenues from Tax Invoice Information

Abstract

In Brazil, the tax on goods and services, known by the acronym ICMS, holds significant prominence in the revenue of the federative units, approximately 90%. Its value depends on economic activity, whose tax information the taxpayers record in electronic invoices issued to the tax agencies. This paper proposes a learning architecture to predict ICMS revenue through a dataset derived from tax information. The learning architecture uses a segmented approach that starts with splitting the training and validation datasets according to a given parameter. After that, the architecture fits several machine learning models for each split subset (segment). Finally, the architecture chooses the fit machine learning model (learning instance) that produces the best prediction result for each segment. These learning instances compose a hybrid instance set to predict the records of a test dataset. The proposed architecture reduced the error compared to the traditional non-segmented approaches tested (by 18.40%) and to the current methodology of the tax agency that supported this research (by 51.90%). The low prediction error suggests that the model holds promise in estimating revenue.