Parameter optimization of support vector regression based on sine cosine algorithm
作者：
Highlights：
• Support vector regression is employed as a time series prediction model.
• A sine cosine algorithm based method is proposed for parameter tuning of SVR.
• The proposed SCA-SVR model is compared to other meta-heuristics algorithms.
• Benchmarks are selected to cover a range of possible practical situations.
• The SCA-SVR method has been demonstrated to be feasible efficiently and reliably.
摘要
•Support vector regression is employed as a time series prediction model.•A sine cosine algorithm based method is proposed for parameter tuning of SVR.•The proposed SCA-SVR model is compared to other meta-heuristics algorithms.•Benchmarks are selected to cover a range of possible practical situations.•The SCA-SVR method has been demonstrated to be feasible efficiently and reliably.
论文关键词：Support vector regression,Sine cosine algorithm,Time series prediction,Parameter optimization
论文评审过程：Received 22 May 2017, Revised 5 August 2017, Accepted 19 August 2017, Available online 24 August 2017, Version of Record 1 September 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.08.038
