Artificial neural networks learning for high-frequency data prediction—big data approach based on genetic and micro-genetic algorithms

Abstract

This study investigates the use of state-of-the-art software tools available on contemporary desktop computing platforms to enhance predictive modeling with machine learning methods. Existing research has not sufficiently examined how efficient utilization of such tools-specifically state-space search reduction, operation parallelization, and mechanisms for escaping local optima-affects model performance when applied to large-scale high-frequency datasets. To address this gap, we introduce new predictive models that explicitly leverage these advanced software capabilities. We further propose strategies for overcoming local optima in neural-network training and for parameter tuning in population-based metaheuristic algorithms used for forecasting high-frequency financial data. Empirical evaluation is conducted on one-minute EUR/CZK exchange rate data from 2018 and on 17 high-frequency Amazon stock price datasets spanning 2005-2021. The results demonstrate that incorporating modern software optimization tools not only improves predictive accuracy but also significantly reduces computation time, making the approach well-suited for real-time forecasting of highly dynamic financial time series.