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⛄ 内容介绍
在自然科学和社会科学各领域中,大量决策问题离不开预测,预测是决策的基础.时间序列是利用现有的历史数据构造模型,进而预测未来.因此,该文首先介绍了定量化的时间序列预测目前采用的一种方法与预测模型,然后在分析时间序列预测相关概念的基础上,研究了其实现方法.
⛄ 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)
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]马飞燕, 李向新. 基于改进麻雀搜索算法-核极限学习机耦合算法的滑坡位移预测模型[J]. 科学技术与工程, 2022(022-005).
