Optimizing LSTM Models for EUR/USD Prediction in the context of reducing energy consumption: An Analysis of Mean Squared Error, Mean Absolute Error and R-Squared

Abstract

The purpose of this study was to develop and evaluate a Long Short-Term Memory (LSTM) model for Forex prediction. The data used was reprocessed and the LSTM model was developed and trained using a supervised learning approach with popular deep learning frameworks. The performance of the model was evaluated using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared. In addition, we examined the literature on energy efficiency, highlighting its potential for reducing computational load and, consequently, energy consumption. We also considered the environmental impact of using such models. The results showed that the LSTM model was effective in Forex prediction and demonstrated superior performance compared to other predictive models. The best model among the several LSTM models evaluated had 90 epochs. These results provide evidence for the efficacy of the LSTM model in Forex prediction and highlight the potential benefits of using deep learning techniques in this field, particularly in terms of energy efficiency and environmental sustainability.