Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs)

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Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting (2010, methodology): “Using hybrid models or combining several models has become a common practice to improve forecasting accuracy and the literature on this topic has expanded dramatically [25].”
ARIMA Model in Predicting Banking Stock Market Data (2018, methodology): “However, only some related works that has used ARIMA model in predicting stock market data for more details refer to (Meyler et al., 1998; Javier et al., 2003; Khashei et al., 2009; Khasheiet. al., 2012; Lee & Ho, 2011; Wang, 2011).”
Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting (2012, background): “…financial time series, autoregressive integrated moving average (ARIMA) models have been popular and are widely chosen for academic research observing the behavior of foreign exchanges and stock markets, because of their statistical properties and forecasting performance (Khashei et al., 2009).”