Abstract
The current study sought to determine whether genetic algorithm can improve forecasting performance and accuracy of machine learning models (ANN, SVR and LSSVR) in a hybrid setting for modelling and forecasting foreign direct investment. The study employed artificial neural network (ANN), support vector regression (SVR) and least squares support vector regression (LSSVR) as benchmark models to determine the forecasting performance of both artificial neural networks based genetic algorithm (ANN-GA) and support vector regression based genetic algorithm (SVR-GA) models respectively. Monthly time series data of foreign direct investment, exchange rate, inflation rate and gross domestic product from Jan 1970 to June 2019 were employed as time series data. The results showed that ANN had a better forecasting accuracy than both SVR and LSSVR respectively, and in the hybrid models SVR-GA had a better forecasting performance than ANN-GA. In the overall it was found that genetic algorithm does not improve the forecasting performance and accuracy of machine learning models as show by MSE, RMSE, MAE, MAPE, and MASE. The study recommends that other evolutionary algorithms be adopted and used to optimize hyper-parameters of machine learning models.
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Rapoo, M.I., Chanza, M., Munapo, E. (2023). Modelling and Forecasting Foreign Direct Investment: A Comparative Application of Machine Learning Based Evolutionary Algorithms Hybrid Models. In: Vasant, P., et al. Intelligent Computing and Optimization. ICO 2023. Lecture Notes in Networks and Systems, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-031-50151-7_3
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