Comparison of LSTM and SARIMA Models for Air Temperature Forecasting in Belém, Amazônia, Pará

This study investigates air temperature forecasting in the city of Belém-PA, comparing the performance of the SARIMA and LSTM models. To this end, daily data from the ERA5-Land database was used and statistical metrics such as mean absolute error (MAE), mean squared error (MSE) and coefficient of determination (R²) were evaluated, supported by ANOVA, Shapiro-Wilk, Levene and Tukey tests. The results indicate that the LSTM model was more accurate, capturing complex patterns better than SARIMA. The findings reinforce the potential of recurrent neural networks in climate modeling and suggest new approaches for improving weather forecasting.