多维时序 | Matlab实现LSTM-Adaboost和LSTM多变量时间序列预测对比
预测效果
基本介绍
多维时序 | Matlab实现LSTM-Adaboost和LSTM多变量时间序列预测对比
模型描述
Matlab实现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;————————————————版权声明：本文为CSDN博主「机器学习之心」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。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
来源地址：https://blog.csdn.net/kjm13182345320/article/details/132515743
