LSTMAdaboostLSTM_0">多维时序 | Matlab实现LSTM-ADABOOST.html" title=LSTM-Adaboost>LSTM-Adaboost和LSTM多变量时间序列预测对比
目录
预测效果
基本介绍
多维时序 | Matlab实现LSTM-ADABOOST.html" title=LSTM-Adaboost>LSTM-Adaboost和LSTM多变量时间序列预测对比
模型描述
Matlab实现LSTM-ADABOOST.html" title=LSTM-Adaboost>LSTM-Adaboost和LSTM多变量时间序列预测对比(完整程序和数据)
1.输入多个特征,输出单个变量;
2.考虑历史特征的影响,多变量时间序列预测;
4.csv数据,方便替换;
5.运行环境Matlab2018b及以上;
6.输出误差对比图。
程序设计
- 完整程序和数据获取方式1:同等价值程序兑换;
- 完整程序和数据获取方式2:私信博主回复Matlab实现LSTM-ADABOOST.html" title=LSTM-Adaboost>LSTM-Adaboost和LSTM多变量时间序列预测对比获取
- 完整程序和数据获取方式3(直接下载):Matlab实现LSTM-ADABOOST.html" title=LSTM-Adaboost>LSTM-Adaboost和LSTM多变量时间序列预测对比。
(32,'OutputMode',"last",'Name','bil4','RecurrentWeightsInitializer','He','InputWeightsInitializer','He')
dropoutLayer(0.25,'Name','drop2')
% 全连接层
fullyConnectedLayer(numResponses,'Name','fc')
regressionLayer('Name','output') ];
layers = layerGraph(layers);
layers = connectLayers(layers,'fold/miniBatchSize','unfold/miniBatchSize');
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 训练选项
if gpuDeviceCount>0
mydevice = 'gpu';
else
mydevice = 'cpu';
end
options = trainingOptions('adam', ...
'MaxEpochs',MaxEpochs, ...
'MiniBatchSize',MiniBatchSize, ...
'GradientThreshold',1, ...
'InitialLearnRate',learningrate, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropPeriod',56, ...
'LearnRateDropFactor',0.25, ...
'L2Regularization',1e-3,...
'GradientDecayFactor',0.95,...
'Verbose',false, ...
'Shuffle',"every-epoch",...
'ExecutionEnvironment',mydevice,...
'Plots','training-progress');
%% 模型训练
rng(0);
net = trainNetwork(XrTrain,YrTrain,layers,options);
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 测试数据预测
% 测试集预测
YPred = predict(net,XrTest,"ExecutionEnvironment",mydevice,"MiniBatchSize",numFeatures);
YPred = YPred';
% 数据反归一化
YPred = sig.*YPred + mu;
YTest = sig.*YTest + mu;
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参考资料
[1] http://t.csdn.cn/pCWSp
[2] https://download.csdn.net/download/kjm13182345320/87568090?spm=1001.2014.3001.5501
[3] https://blog.csdn.net/kjm13182345320/article/details/129433463?spm=1001.2014.3001.5501