ghimohammadr/Metaheuristics_SVR
The performance of SVR models highly depends upon the appropriate choice of SVR parameters. Here, different metaheuristic algorithms are used to tune the hyperparameters.
This tool helps financial analysts and quantitative traders improve the accuracy of their stock market predictions. It takes historical stock market data and automatically optimizes the settings for Support Vector Regression (SVR) models, producing more reliable forecasts of future stock behavior. The output is a finely tuned SVR model ready for making predictions.
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Use this if you are a financial analyst or quantitative trader needing to build highly accurate stock market forecasting models using Support Vector Regression.
Not ideal if you prefer not to use MATLAB or are not working with Support Vector Regression for your predictive modeling.
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MATLAB
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Last pushed
Oct 04, 2023
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