Support vector regression with chaos-based firefly algorithm for stock market price forecasting

By Ahmad Kazem, Ebrahim Sharifi, F. Hussain, Morteza Saberi, Omar Khadeer Hussain. Applied Soft Computing, 2013

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Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) (2021, background): “For more versions of FFA in various applications of feature selection, interest researchers can be found in [156]–[159].”
Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting (2014, methodology): “Concerning the parameter selection of MSVR, the superiority of firefly algorithm (FA) against GA and PSO in existing studies [29-31] motivates us to use the FA for parameters selection of MSVR.”
An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information (2018, methodology): “Reference [24] presented support vector regression methods optimized by chaotic firefly algorithm outperforms several methods of SVR for NASDAQ quotes, Intel (from 9/12/2007 to 11/11/2010), National Bank shares (from 6/27/2008 to 8/29/2011) and Microsoft (from 9/12/2007 to 11/11/2011) daily closed s”
Firefly algorithm with neighborhood attraction (2017, methodology): “[25] presented a new FA for stock market price forecasting, which employs two new strategies, namely chaotic FA and support vector regression (SVR) [19].”