  Support Vector Regression (SVR) has achieved high performance on forecasting
future behavior of random systems. However, the performance of SVR models
highly depends upon the appropriate choice of SVR parameters. In this study, a
novel BOA-SVR model based on Butterfly Optimization Algorithm (BOA) is
presented. The performance of the proposed model is compared with eleven other
meta-heuristic algorithms on a number of stocks from NASDAQ. The results
indicate that the presented model here is capable to optimize the SVR
parameters very well and indeed is one of the best models judged by both
prediction performance accuracy and time consumption.
