Support Vector Regression Parameters Optimization using Golden Sine Algorithm and its application in stock market
This work addresses a specific problem for researchers and practitioners in financial forecasting by providing an incremental improvement in parameter optimization for SVR models.
The authors tackled the challenge of selecting optimal parameters for Support Vector Regression (SVR) by proposing a novel Golden Sine Algorithm (GSA) for parameter tuning, and demonstrated its efficiency by achieving competitive accuracy and computing time compared to eleven other meta-heuristic algorithms on historical stock price data.
Support vector machine modeling is a new approach in machine learning for classification showing good performance on forecasting problems of small samples and high dimensions. Later, it promoted to Support Vector Regression (SVR) for regression problems. A big challenge for achieving reliable is the choice of appropriate parameters. Here, a novel Golden sine algorithm (GSA) based SVR is proposed for proper selection of the parameters. For comparison, the performance of the proposed algorithm is compared with eleven other meta-heuristic algorithms on some historical stock prices of technological companies from Yahoo Finance website based on Mean Squared Error and Mean Absolute Percent Error. The results demonstrate that the given algorithm is efficient for tuning the parameters and is indeed competitive in terms of accuracy and computing time.