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.

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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.