Evaluation of time series forecasting models applied to municipal expenses in a city in Ceará - Gomes, José Wivo; Santos, João Evangelista Dantas dos; Santos, Rodolfo de Sousa (Brazilian Journal of Production Engineering, 2025)
To meet the complex financial planning needs of municipalities with high spending volatility, such as those in the interior of Ceará, it is necessary to have predictive models capable of accurately projecting expenses and optimizing resources. The comparison between the Auto ETS and Auto ARIMA models in the performance of expense forecasts was the central question of this study, which verified the results using historical data from 2018 to 2023. The evaluation metrics correspond to MAE, RMSE, MSE, MAPE, R² and correlation, associated with qualitative analysis of models. The results, in this sense, indicate the best performance of Auto ETS in absolute errors, with MAE of 21,510,499.88 and RMSE of 27,275,560.60 and explanation of variance with R² of 0.9139. Meanwhile, Auto ARIMA presents the best relative accuracy, with MAPE = 23.63%. However, the two models have an extremely high correlation, above 0.97, between the actual and predicted values. When testing the forecasts, the models captured historical patterns, demonstrating high adherence to the actual values. For the forecast range from 2024 to 2025, the estimates maintained patterns and seasonal variations in line with the past; Auto ETS smoothed out the most extreme peaks and proved to be more reliable; while Auto ARIMA handled the small variations better, contributing to the city's financial planning.
Keywords: Machine Learning; Forecasting; Statistical Models; Resource Planning