时序预测 | MATLAB实现贝叶斯优化GRU时间序列预测(BO-GRU/Bayes-GRU)

时序预测 | MATLAB实现贝叶斯优化GRU时间序列预测----贝叶斯优化门控循环单元(BO-GRU/Bayes-GRU)

基本介绍

本次运行测试环境MATLAB2020b；
MATLAB实现贝叶斯优化GRU时间序列预测(BO-GRU/Bayes-GRU),贝叶斯优化门控循环单元)；

预测效果

程序设计

• 完整程序和数据下载：MATLAB实现贝叶斯优化GRU时间序列预测(BO-GRU/Bayes-GRU)

%% 数据导入
data.CompleteData = readtable('data.xlsx','Sheet',1);
data.seriesdataHeder = data.CompleteData.Properties.VariableNames(1,:);
data.seriesdata = table2array(data.CompleteData(:,:));
disp('数据导入成功');
data.isDataRead = true;      
if strcmpi(opt.dataPreprocessMode,'None')
        data.x = data.seriesdata;
elseif strcmpi(opt.dataPreprocessMode,'Data Normalization')
% 数据规范化     
for i=1:size(data.seriesdata,2)
       data.x(:,i) = (data.seriesdata(:,i) -min(data.seriesdata(:,i)))./ (max(data.seriesdata(:,i))-min(data.seriesdata(:,i)));
end
% 数据表转化   
elseif strcmpi(opt.dataPreprocessMode,'Data Standardization')
for i=1:size(data.seriesdata,2)
    x.mu(1,i)   = mean(data.seriesdata(:,i),'omitnan');
    x.sig(1,i)  = std (data.seriesdata(:,i),'omitnan');
    data.x(:,i) = (data.seriesdata(:,i) - x.mu(1,i))./ x.sig(1,i);
end

figure('Name','NormilizedInputData','NumberTitle','off');
plot(data.x,'--','linewidth',2); 
title({['Mean = ' num2str(mean(data.x)) ', STD = ' num2str(std(data.x)) ];});
end
    
%% 数据准备
%  GRU数据准备
% 网络时间延迟设定
Delays = opt.Delays;
x = data.x';
T = size(x,2);
MaxDelay = max(Delays);
Range = MaxDelay+1:T;
X= [];
for d = Delays
    X=[X; x(:,Range-d)];
end
Y = x(:,Range);
data.X  = X;
data.Y  = Y;

往期精彩

MATLAB实现贝叶斯优化CNN-LSTM时间序列预测(股票价格预测)
MATLAB实现Bayes贝叶斯优化LSTM(长短期记忆神经网络)时间序列预测
MATLAB实现贝叶斯优化CNN-GRU时间序列预测(股票价格预测)

参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/127895932?spm=1001.2014.3001.5501
[2] https://blog.csdn.net/kjm13182345320/article/details/127894261?spm=1001.2014.3001.5501