[Regression prediction] Gaussian process regression based on GPML toolbox with matlab code

1 Introduction

Aiming at the low accuracy of traditional network traffic forecasting, in order to obtain ideal network traffic forecasting results, a network traffic forecasting model based on Gaussian Process Regression (GPR) is proposed. The model first calculates the delay time and embedding dimension, and constructs a Gaussian process regression model.Then use the Gaussian process regression to learn the network flow training set, and use the invasive weed optimization to optimize the parameters of the Gaussian process regression; finally, use the classic network flow test set to test the performance of the model.The experimental results show that the Gaussian process regression model improves the prediction accuracy of network traffic.

2 Emulation code

%%%%%%%%%% Gaussian Process Regression (GPR) %%%%%%%%%

% Demo: prediction using GPR

% ---------------------------------------------------------------------%

clc

close all

clear all

addpath(genpath(pwd))

% load data

%{

x : training inputs

y : training targets

xt: testing inputs

yt: testing targets

%}

% multiple input-multiple output

load('./data/data_2.mat')

% Set the mean function, covariance function and likelihood function

% Take meanConst, covRQiso and likGauss as examples

meanfunc = @meanConst;

covfunc = @covRQiso;

likfunc = @likGauss;

% Initialization of hyperparameters

hyp = struct('mean', 3, 'cov', [2 2 2], 'lik', -1);

% meanfunc = [];

% covfunc = @covSEiso;

% likfunc = @likGauss;

%

% hyp = struct('mean', [], 'cov', [0 0], 'lik', -1);

% Optimization of hyperparameters

hyp2 = minimize(hyp, @gp, -5, @infGaussLik, meanfunc, covfunc, likfunc,x, y);

% Regression using GPR

% yfit is the predicted mean, and ys is the predicted variance

[yfit ys] = gp(hyp2, @infGaussLik, meanfunc, covfunc, likfunc,x, y, xt);

% Visualization of prediction results

% First output

plotResult(yt(:,1), yfit(:,1))

% Second output

plotResult(yt(:,2), yfit(:,2))

3 Run Results

4 References

[1]Li Zhengang. Network Traffic Prediction Model Based on Gaussian Process Regression [J]. Computer Applications, 2014.

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