Gaussian Process Regression (GPR)

• Category: Deep Learning
• Development language: python
• Operating platform / Operating system: Windows 7以上
• Compilation tools and environment: PyCharm、Anaconda
• GitHub download path: https://gitee.com/three-poles/DeepLearning

Method description:

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.

Installation: python

Operation mode:

Input variable: time series data

Output variable: predicted time series data

QR code:

• 安装方式：

  安装python
  
  运行方式：

  在PyCharm中打开脚本文件即可运行
  
  输入变量：

  时间序列数据
  
  输出变量：

  预测时间序列数据
  
  二维码：