摘要
Based on the classical Gaussian process （GP） model, we propose a multi-scale Gaussian process （MGP） model to predict the existence of chaotic time series. The MGP employs a covariance function that is constructed by a scaling function with its different dilations and translations, ensuring that the optimal hyperparameter is easy to determine. Moreover, the scaling function with its different dilations and translations can form a set of complete bases, resulting in the fact that the MGP can acquire better prediction performance than the GP. The experiments can lead to the following conclusions： （i） The MGP gives a relatively better prediction performance in comparison with the classical GP model. （ii） The prediction performance of the MGP is competitive with support vector machine （SVM）. They give better performance as compared to the radial basis function networks.
Based on the classical Gaussian process （GP） model, we propose a multi-scale Gaussian process （MGP） model to predict the existence of chaotic time series. The MGP employs a covariance function that is constructed by a scaling function with its different dilations and translations, ensuring that the optimal hyperparameter is easy to determine. Moreover, the scaling function with its different dilations and translations can form a set of complete bases, resulting in the fact that the MGP can acquire better prediction performance than the GP. The experiments can lead to the following conclusions： （i） The MGP gives a relatively better prediction performance in comparison with the classical GP model. （ii） The prediction performance of the MGP is competitive with support vector machine （SVM）. They give better performance as compared to the radial basis function networks.
基金
Supported by the National Natural Science Foundation of China under Grant No 60602034.
