?作者簡介：熱愛科研的Matlab仿真開發(fā)者，修心和技術(shù)同步精進，matlab項目合作可私信。

??個人主頁：Matlab科研工作室

??個人信條：格物致知。

? 內(nèi)容介紹

在自然科學(xué)和社會科學(xué)各領(lǐng)域中,大量決策問題離不開預(yù)測,預(yù)測是決策的基礎(chǔ).時間序列是利用現(xiàn)有的歷史數(shù)據(jù)構(gòu)造模型,進而預(yù)測未來.因此,該文首先介紹了定量化的時間序列預(yù)測目前采用的一種方法與預(yù)測模型,然后在分析時間序列預(yù)測相關(guān)概念的基礎(chǔ)上,研究了其實現(xiàn)方法.

? 部分代碼

%% 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));

? 運行結(jié)果

編輯

編輯

? 參考文獻

[1]馬飛燕, 李向新. 基于改進麻雀搜索算法-核極限學(xué)習(xí)機耦合算法的滑坡位移預(yù)測模型[J]. 科學(xué)技術(shù)與工程, 2022(022-005).

? 完整代碼

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