Redes neuronales recurrentes y modelos arima para el pronóstico de la inflación en el Perú - Chilón Ayay, Anghie Lizzeth (2023)
The purpose of this research is to compare the ARIMA methodology and Recurrent _x000D_ Neural Networks to find the best model to forecast Inflation in Peru. The research consisted _x000D_ of a study sample from January 2000 to December 2021._x000D_ When designing the series model for each series, a forecast was made for all the _x000D_ months of the year 2022, which were compared with the real data to determine which _x000D_ methodology makes a better forecast. The results indicated that the previous LSTM recurrent _x000D_ neural network model obtained a lower forecast evaluation error compared to the ARIMA _x000D_ methodology, for which the RMSE, MAE, MAPE, EMC and MPE indicators were used