“Models for Forecasting ICMS Revenue in Minas Gerais Using LSTM Neural Networks”
ICMS, Minas Gerais, Tax Forecasting, LSTM Neural Networks, Fiscal Management.
This dissertation investigates the use of advanced models for forecasting ICMS revenue in Minas Gerais, employing Long Short-Term Memory Recurrent Neural Networks (LSTMs). The study proposes an innovative approach in tax data analysis, highlighting the ability of LSTMs to capture and learn complex temporal patterns. The dataset includes historical revenue information, and other relevant economic variables. The methodology involves training and validating the models based on this data, resulting in an enhancement of tax forecasting techniques. The results demonstrate the effectiveness of LSTMs in anticipating ICMS revenue, providing valuable insights for more efficient fiscal management.