Um algoritmo genético transgênico para otimização de hiperparâmetros de redes long short-term memory aplicado à previsão de preços de café arábica

Abstract

Financial time series forecasting remains a challenging problem due to the high vo- latility, nonlinearity, and complexity of financial markets. This dissertation investigates the use of Long Short-Term Memory (LSTM) neural networks to forecast Arabica coffee futures closing prices, aiming to analyze the contribution of evolutionary hyperparameter optimization to the development of models capable of balancing predictive performance, financial performance, and computational cost. Three approaches were compared: a ba- seline LSTM with fixed hyperparameters, an LSTM optimized by a Genetic Algorithm (LSTM-GA), and an LSTM optimized by an adapted Transgenetic Genetic Algorithm (LSTM-TGA). The proposed methodology incorporates a probabilistic genetic history and four transgenetic modules into the evolutionary process in order to modify the se- arch dynamics for promising hyperparameter configurations. Experiments were conducted using historical closing prices of Arabica coffee futures contracts (KC=F) considering th- ree temporal resolutions: 4-hour, daily, and weekly. Model evaluation included predictive accuracy metrics, convergence and population diversity analyses, architectural complexity, computational cost, architectural efficiency, and financial validation through backtesting. The results demonstrate that evolutionary optimization effectively automated hyperpa- rameter selection, producing competitive neural architectures and showing that different temporal resolutions require distinct configurations. Furthermore, improvements in pre- dictive performance were not necessarily associated with superior financial performance, highlighting the importance of an integrated evaluation involving predictive accuracy, financial performance, architectural complexity, and computational cost. The adapted TGA proved to be a competitive optimization strategy by generating structurally simpler architectures, preserving population diversity, and producing models with high architec- tural efficiency. It is concluded that the proposed methodology represents a promising approach for automatic LSTM hyperparameter optimization, contributing to the advan- cement of evolutionary optimization applied to Artificial Intelligence and financial time series forecasting.