  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.
