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⛄ 内容介绍
石油是世界经济政治发展不能缺少的重要能源之一,它在世界经济发展中产生了重要的影响。减少其负面影响的办法就是了解并掌握石油价格的变化,因此目前全球热衷于的话题就是石油价格。石油价格被种种因素影响,包括供需、经济、国际政治、军事、外交等。由于石油价格的不确定性、复杂非线性、多种影响因素,非数值型的信息,数值型信息的数据噪声也十分大。由于传统石油价格预测方法不可依靠或预测结果精度较低。故此,学术和工业界研究者的目标就是找到一种预测模型,使其研究精度相对较高、性能等各方面均相对较好,同时还可以广泛使用。基于贝叶斯网络优化门控循环单元BO-GRU实现时间序列数据预测附matlab代码。
⛄ 部分代码
%% Mackey Glass Time Series Prediction Using Least Mean Square (LMS)
% Author: SHUJAAT KHAN
clc
clear all
close all
%% Loading Time series data
% I generated a series y(t) for t = 0,1, . . . ,3000, using
% mackey glass series equation with the following configurations:
% b = 0.1, a = 0.2, Tau = 20, and the initial conditions y(t - Tau) = 0.
load Dataset\Data.mat
time_steps=2;
teacher_forcing=1; % recurrent ARMA modelling with forced desired input after defined time steps
%% Training and Testing datasets
% For training
Tr=Data(100:2500,1); % Selecting a Interval of series data t = 100~2500
Yr(Tr)=Data(Tr,2); % Selecting a chuck of series data y(t)
% For testing
Ts=Data(2500:3000,1); % Selecting a Interval of series data t = 2500~3000
Ys(Ts)=Data(Ts,2); % Selecting a chuck of series data y(t)
%% LMS Parameters
eta=5e-3; % Learning rate
M=1; % Order of LMS filter
U=zeros(M+1,1); % Initial values of taps
W=zeros(M+1,1); % Initial weight of LMS
MSE=[]; % Initial mean squared error (MSE)
%% Learning weights of LMS (Training)
tic % start
for t=Tr(1):Tr(end)-time_steps
U(1:end-1)=U(2:end); % Shifting of tap window
if (teacher_forcing==1)
if rem(t,time_steps)==0 || (t==Tr(1))
U(end)=Yr(t); % Input (past/current samples)
else
U(end)=Yp(t-1); % Input (past/current samples)
end
else
U(end)=Yr(t); % Input (past/current samples)
end
Yp(t)=W'*U; % Predicted output
e(t)=Yr(t+time_steps)-Yp(t); % Error in predicted output
W=W+eta*e(t)*U; % Weight update rule of LMS
E(t)=e(t).^2; % Current mean squared error (MSE)
end
training_time=toc; % total time including training and calculation of MSE
%% Prediction of a next outcome of series using previous samples (Testing)
tic % start
U=U*0; % Reinitialization of taps (optional)
for t=Ts(1):Ts(end)-time_steps+1
U(1:end-1)=U(2:end); % Shifting of tap window
if (teacher_forcing==1)
if rem(t,time_steps)==0 || (t==Ts(1))
U(end)=Ys(t); % Input (past/current samples)
else
U(end)=Yp(t-1); % Input (past/current samples)
end
else
U(end)=Ys(t); % Input (past/current samples)
end
Yp(t)=W'*U; % Calculating output (future value)
e(t)=Ys(t+time_steps-1)-Yp(t); % Error in predicted output
E(t)=e(t).^2; % Current mean squared error (MSE)
end
testing_time=toc; % total time including testing and calculation of MSE
%% Results
figure(1)
plot(Tr,10*log10(E(Tr))); % MSE curve
hold on
plot(Ts(1:end-time_steps+1),10*log10(E(Ts(1:end-time_steps+1))),'r'); % MSE curve
grid minor
title('Cost Function');
xlabel('Iterations (samples)');
ylabel('Mean Squared Error (MSE)');
legend('Training Phase','Test Phase');
figure(2)
plot(Tr(2*M:end),Yr(Tr(2*M:end))); % Actual values of mackey glass series
hold on
plot(Tr(2*M:end),Yp(Tr(2*M:end))','r') % Predicted values during training
plot(Ts,Ys(Ts),'--b'); % Actual unseen data
plot(Ts(1:end-time_steps+1),Yp(Ts(1:end-time_steps+1))','--r'); % Predicted values of mackey glass series (testing)
xlabel('Time: t');
ylabel('Output: Y(t)');
title('Mackey Glass Time Series Prediction Using Least Mean Square (LMS)')
ylim([min(Ys)-0.5, max(Ys)+0.5])
legend('Training Phase (desired)','Training Phase (predicted)','Test Phase (desired)','Test Phase (predicted)');
mitr=10*log10(mean(E(Tr))); % Minimum MSE of training
mits=10*log10(mean(E(Ts(1:end-time_steps+1)))); % Minimum MSE of testing
display(sprintf('Total training time is %.5f, \nTotal testing time is %.5f \nMSE value during training %.3f (dB),\nMSE value during testing %.3f (dB)', ...
training_time,testing_time,mitr,mits));
⛄ 运行结果
⛄ 参考文献
[1]周洁超. 基于贝叶斯网络的时间序列预测[D]. 大连海事大学, 2016.
