Parameter optimization of support vector regression based on sine cosine algorithm

- 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.

Time series prediction is an important part of data-driven based prognostics which are mainly based on the massive sensory data with less requirement of knowing inherent system failure mechanisms. Support Vector Regression (SVR) has achieved good performance in forecasting problems of small samples and high dimensions. However, the SVR parameters have a significant influence on forecasting performance of SVR. In our current work, a novel SCA-SVR model has been presented where sine cosine algorithm (SCA) is used to select the penalty and kernel parameters in SVR, so that the generalization performance on unknown data can be improved. To validate the proposed model, the results of the SCA-SVR algorithm were compared with those of grid search and some other meta-heuristics optimization algorithms on common used benchmark datasets. The experimental results proved that the proposed model is capable to find the optimal values of the SVR parameters and can yield promising results.