Hybrid of Kalman filter and support vector regression for time series prediction
What is it about?
In this article, a new algorithm for prediction of respiratory motion is proposed. The proposed algorithm is a hybrid of a model-bassed Kalman filter and learning-based support vector regression. The algorithm exploit the benefit of both the approaches and yield better performance than individual algorithms.
Why is it important?
The proposed algorithm is applied for the prediction of respiratory motion. The results of our algorithm are compared with state-or-the-art artificial neural networks (ANN) and support vector regression (SVR). Our proposed algorithm significantly outperform both the ANN as well SVR algorithm for the prediction of respiratory motion.
The following have contributed to this page: Waqas Bukhari