Kernel function construction method and device, data prediction method and device, equipment, and storage medium

Abstract

The invention relates to a kernel function construction method and device, a data prediction method and device, equipment, and a storage medium. The kernel function construction method includes the following steps: obtaining a preset variance parameter and a preset frequency translation parameter; constructing a kernel function according to the preset variance parameter and the preset frequency translation parameter; performing characteristic decomposition on a covariance matrix of the kernel function to obtain a decomposition result; and optimizing a hyper-parameter of the kernel function bya maximized edge log-likelihood function to obtain an optimal kernel function according to the decomposition result. Hyper-parameter optimization prevents from being fallen into a locally optimal solution, so that the accuracy of the optimal kernel function obtained by the optimization is higher, and the accuracy of a gaussian process regression model constructed by the optimal kernel function ishigher, and therefore, when the gaussian process regression model is applied to data prediction, the accuracy of an obtained prediction result is higher.

Description

technical field [0001] The present application relates to the field of computer information technology, in particular to a kernel function construction method, device, computer equipment and storage medium, and a data prediction method, device, computer equipment and storage medium. Background technique [0002] Due to the excellent performance of Gaussian Processes in function approximation and the uncertainty bound of the model, Gaussian process regression models are widely used. For example, the forecast of residents’ daily living data (such as water, electricity, and gas consumption); the forecast of the supply or demand of taxis or online car-hailing in a certain area in a certain period of time in urban traffic; the time-sharing of uplink and downlink data traffic in the network Forecasting; real-time forecasting of stock trends; urban weather and concentration forecasting of pollutants (PM2.5, PM10, etc.). [0003] The focus of the Gaussian process regression model i...

AI Technical Summary

Problems solved by technology

Since the process of minimizing hyperparameters is easy to fall into a local optimal solution, the accuracy of the Gaussian process regression model constructed by the spectral mixture kernel function is low, so when the Gaussian process regression model is applied to data prediction, the prediction results obtained is less accurate

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