Abstract
In recent years, Artificial Intelligence (AI) methods have proven to be successful tools for forecasting in the sectors of business, finance, medical science and engineering. In this study, we employ a Genetic Algorithm (GA) to select the optimal variable weights in order to predict exchange rates; subsequently, Genetic Algorithms, Particle Swam Optimization (PSO) and Back Propagation Network (BPN) are utilized to construct three models: GA__GA, GA__PSO, GA__BPN to compare results with a traditional regression model. Fundamentally, we expect enhanced variable selection to provide improved forecasting performance. The results of our experiments indicate that the GA__GA model achieves the best forecasting performance and is highly consistent with the actual data.
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Chang, JF. (2011). Exchange Rate Forecasting with Hybrid Genetic Algorithms. In: Chen, SH., Terano, T., Yamamoto, R. (eds) Agent-Based Approaches in Economic and Social Complex Systems VI. Agent-Based Social Systems, vol 8. Springer, Tokyo. https://doi.org/10.1007/978-4-431-53907-0_4
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DOI: https://doi.org/10.1007/978-4-431-53907-0_4
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-53906-3
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