Gaussian process regression (GPR) is a non parametric model that uses Gaussian process (GP) prior to regression analysis of data. The model assumption of GPR includes regression residual and Gaussian process. The essence of its solution process is Bayesian inference. If the prior form of Gaussian process is not limited, GPR is a universal approximation of any objective function in theory. In addition, GPR can provide a posteriori of the prediction results, and the posteriori has an analytical form when the regression residual is an independent identically distributed normal distribution. Therefore, GPR is a probability model with universality and analyzability.
Input variable: time series data
Output variable: predicted time series data
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LinksNational Tibetan Plateau Data Center