Recursive prediction algorithm for non-stationary Gaussian Process

Gaussian Process is a theoretically rigorous model for prediction problems. One of the deficiencies of this model is that its original exact inference algorithm is computationally intractable. Therefore, its applications are limited in the field of real-time online predictions. In this paper, a recursive prediction algorithm based on the Gaussian Process model is proposed. In recursive algorithms, the computational time of the next step can be greatly reduced by utilizing the intermediate results of the current step. The proposed recursive algorithm accelerates the prediction and avoids the loss of accuracy at the same time. Experiments are done on an ultra-short term electric load data set and the results are demonstrated to show the accuracy and efficiency of the new algorithm.