時(shí)序預(yù)測(cè) | MATLAB實(shí)現(xiàn)基于CNN-LSTM卷積長(zhǎng)短期記憶神經(jīng)網(wǎng)絡(luò)的時(shí)間序列預(yù)測(cè)-遞歸預(yù)測(cè)未來(lái)(多指標(biāo)評(píng)價(jià))
預(yù)測(cè)結(jié)果
基本介紹
MATLAB實(shí)現(xiàn)基于CNN-LSTM卷積長(zhǎng)短期記憶神經(jīng)網(wǎng)絡(luò)的時(shí)間序列預(yù)測(cè)-遞歸預(yù)測(cè)未來(lái)(多指標(biāo)評(píng)價(jià))
1.MATLAB實(shí)現(xiàn)基于CNN-LSTM卷積長(zhǎng)短期記憶神經(jīng)網(wǎng)絡(luò)的時(shí)間序列預(yù)測(cè)-遞歸預(yù)測(cè)未來(lái)(多指標(biāo)評(píng)價(jià))；
2.運(yùn)行環(huán)境Matlab2020及以上，data為數(shù)據(jù)集，單變量時(shí)間序列預(yù)測(cè)；
3.遞歸預(yù)測(cè)未來(lái)數(shù)據(jù)，可以控制預(yù)測(cè)未來(lái)大小的數(shù)目，適合循環(huán)性、周期性數(shù)據(jù)預(yù)測(cè)；
4.命令窗口輸出R2、MAE、MAPE、MBE、MSE等評(píng)價(jià)指標(biāo)；文章來(lái)源：http://www.zghlxwxcb.cn/news/detail-652579.html
程序設(shè)計(jì)
- 完整程序和數(shù)據(jù)獲取方式：私信博主回復(fù)MATLAB實(shí)現(xiàn)基于CNN-LSTM卷積長(zhǎng)短期記憶神經(jīng)網(wǎng)絡(luò)的時(shí)間序列預(yù)測(cè)-遞歸預(yù)測(cè)未來(lái)(多指標(biāo)評(píng)價(jià))；
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 數(shù)據(jù)集分析
outdim = 1; % 最后一列為輸出
num_size = 0.7; % 訓(xùn)練集占數(shù)據(jù)集比例
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 劃分訓(xùn)練集和測(cè)試集
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);
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 數(shù)據(jù)歸一化
[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);
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原文鏈接：https://blog.csdn.net/kjm13182345320/article/details/132093256
參考資料
[1] https://blog.csdn.net/kjm13182345320/article/details/129036772?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/128690229文章來(lái)源地址http://www.zghlxwxcb.cn/news/detail-652579.html
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