Comparative analysis of machine learning models in predicting demand for clotheslines installation - Marchese, Bruno Grizzo; Sozzo, Bruna (Revista Terra & Cultura: Cadernos de Ensino e Pesquisa; Vol 40 No especial (2024): Computação, 2024)
This article aims to compare the accuracy of the Prophet machine learning model and the Long Short Term Memory (LSTM) recurrent neural network in predicting the demand for a clothesline installation service based on a historical record of installations from 2004 to the end of 2021. To conduct this evaluation, LSTM and Prophet models are created to forecast the demand based on the gathered data. Finally, performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) are analyzed to compare the accuracy of the two methods.