High Innovation |

Princeps innovatio |

2024-07-12

Tabula contentorum

Effectus album

Insert imaginem descriptionis hic

basic introductio

High innovation | CEEMDAN-VMD-GRU-Attentio dualis compositionis + gatum recurrentis unitatis + attentio mechanismi multivariata series temporis praenuntiatio
Haec charta secundariam compositionis methodum in CEEMDAN proponit, quae seriem a CEEMDAN per entropytam decompositam reformat.

Exemplar design

1.Matlab instrumenti CEEMDAN-VMD-GRU Operatio dualis compositionis + ansa unitatum ansa + multiplex machinationes attentionistempus seriem forecasting(The source code and data)

2. CEEMDAN corrumpit, entropiae specimen computat, kmeans entropiae specimen innitentem facit, VMD vocat ad summum frequentiae componentium bis dissolutum, et utitur componentia alta frequentia et anteriori parte decomposita VMD sicut convolutionis unitatis cyclicae gatum.Operam mechanism exemplarScopum outputs separatim praedictum est ac deinde adiectum est.

3. Multi-variabilis unius rei gratia considera influxum notarum historicarum! Indicatores aestimationes includunt R2, MAE, RMSE, MAPE, etc.

4. Algorithmus novus est. Exemplum CEEMDAN-VMD-GRU Operam cum notitia processus altioris accurationis et notitias trends et mutationes indagare potest. Exemplar VMD tractat notitias nonlinearias, non statarias et implicatas et melius facit quam series EMD. Ergo notitia restructa per exemplum VMD resolutum est ut accurationem exemplaris emendare possit.

5. Adhiberi potest per directe reponens Praecedo datae. Annotationes sunt clarae et novitiis aptae. Potes directe currere tabella principalis ut graphium emittat cum uno clic.

6. Codicis lineamenta: programmatio parametri, parametri facile mutari possunt, notiones programmandi notiones sunt clarae et commentationes explicatae sunt.

Reference 1

Insert imaginem descriptionis hic

Relatio 2
Reference 3
notitia paro

programming

Complete programma privata nuntium blogger responsumCEEMDAN-VMD-GRU Operatio compositionis dualis + gatum recurrentis unitatis + attentio mechanismi multivariati series temporis praenuntiatio。



%%  清空环境变量
warning off             % 关闭报警信息
close all               % 关闭开启的图窗
clear                   % 清空变量
clc                     % 清空命令行

%%  划分训练集和测试集
P_train = res(1: num_train_s, 1: f_)';
T_train = res(1: num_train_s, f_ + 1: end)';
M = size(P_train, 2);

P_test = res(num_train_s + 1: end, 1: f_)';
T_test = res(num_train_s + 1: end, f_ + 1: end)';
N = size(P_test, 2);

%%  数据归一化
[P_train, ps_input] = mapminmax(P_train, 0, 1);
P_test = mapminmax('apply', P_test, ps_input);

[t_train, ps_output] = mapminmax(T_train, 0, 1);
t_test = mapminmax('apply', T_test, ps_output);

%%  数据平铺
P_train =  double(reshape(P_train, f_, 1, 1, M));
P_test  =  double(reshape(P_test , f_, 1, 1, N));

t_train = t_train';
t_test  = t_test' ;

%%  数据格式转换
for i = 1 : M
    p_train{i, 1} = P_train(:, :, 1, i);
end

for i = 1 : N
    p_test{i, 1}  = P_test( :, :, 1, i);
end

 
%%  参数设置
options = trainingOptions('adam', ...      % Adam 梯度下降算法
    'MaxEpochs', 100, ...                  % 最大训练次数 
    'InitialLearnRate', 0.01, ...          % 初始学习率为0.01
    'LearnRateSchedule', 'piecewise', ...  % 学习率下降
    'LearnRateDropFactor', 0.1, ...        % 学习率下降因子 0.1
    'LearnRateDropPeriod', 70, ...         % 经过训练后 学习率为 0.01*0.1
    'Shuffle', 'every-epoch', ...          % 每次训练打乱数据集
    'Verbose', 1);
figure
subplot(2,1,1)
plot(T_train,'k--','LineWidth',1.5);
hold on
plot(T_sim_a','r-','LineWidth',1.5)
legend('真实值','预测值')
title('CEEMDAN-VMD-CNN-GRU-Attention训练集预测效果对比')
xlabel('样本点')
ylabel('数值')
subplot(2,1,2)
bar(T_sim_a'-T_train)
title('CEEMDAN-VMD-GRU-Attention训练误差图')
xlabel('样本点')
ylabel('数值')

disp('…………测试集误差指标…………')
[mae2,rmse2,mape2,error2]=calc_error(T_test,T_sim_b');
fprintf('n')


figure
subplot(2,1,1)
plot(T_test,'k--','LineWidth',1.5);
hold on
plot(T_sim_b','b-','LineWidth',1.5)
legend('真实值','预测值')
title('CEEMDAN-VMD-GRU-Attention测试集预测效果对比')
xlabel('样本点')
ylabel('数值')
subplot(2,1,2)
bar(T_sim_b'-T_test)
title('CEEMDAN-VMD-GRU-Attention测试误差图')
xlabel('样本点')
ylabel('数值')


1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86

References

[1] https://hmlhml.blog.csdn.net/article/details/135536086?spm=1001.2014.3001.5502
[2] https://hmlhml.blog.csdn.net/article/details/137166860?spm=1001.2014.3001.5502
[3] https://hmlhml.blog.csdn.net/article/details/132372151

Technology sharing