Comparative Analysis of Hyperparameter Optimization Methods for LSTM in Cryptocurrency Price Prediction: An Application to TRX–USD

Abstract

The rapid growth of cryptocurrencies increases the demand for accurate forecasting models to support investment decisions and automated trading systems. This study analyzes and compares the performance of several hyperparameter optimization methods applied to a Long Short-Term Memory (LSTM) model for predicting the price of TRX–USD. The dataset consists of 2,096 daily historical records obtained from the Binance platform, including open, high, low, close, volume, and percentage change, with the closing price selected as the forecasting target. A baseline LSTM model was evaluated against six optimization techniques: Grid Search, Random Search, Bayesian Optimization (Hyperopt), Optuna, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Experimental results show that GA provides the best performance with an R² score of 0.88, MAE of 0.0123, RMSE of 0.0189, and a validation loss of 0.069. In contrast, Random Search yields the lowest performance, achieving an R² of only 0.2979. These findings highlight significant performance gaps among optimization strategies and demonstrate the superiority of metaheuristic-based approaches over conventional tuning methods. This research contributes to the advancement of computational intelligence by providing empirical evidence on the effectiveness of hyperparameter optimization techniques for deep learning–based time series forecasting, particularly in high-volatility financial environments.