LSTM单步预测自己预测自己Matlab代码
时间: 2023-10-07 10:14:14 浏览: 48
以下是一个简单的 Matlab 代码示例,用于使用 LSTM 模型进行单步预测并预测自身:
```matlab
% 加载数据
data = load('data.mat');
Xtrain = data.Xtrain;
Ytrain = data.Ytrain;
Xtest = data.Xtest;
Ytest = data.Ytest;
% 创建 LSTM 模型
numFeatures = size(Xtrain,2);
numResponses = size(Ytrain,2);
numHiddenUnits = 200;
layers = [ ...
sequenceInputLayer(numFeatures)
lstmLayer(numHiddenUnits,'OutputMode','last')
fullyConnectedLayer(numResponses)
regressionLayer];
% 训练 LSTM 模型
options = trainingOptions('adam', ...
'MaxEpochs',100, ...
'MiniBatchSize',50, ...
'InitialLearnRate',0.01, ...
'GradientThreshold',1, ...
'Shuffle','every-epoch', ...
'Verbose',false);
net = trainNetwork(Xtrain',Ytrain',layers,options);
% 单步预测
numTimeStepsTest = size(Xtest,1);
YPredicted = zeros(numTimeStepsTest,numResponses);
for i = 1:numTimeStepsTest
[net,YPredicted(i,:)] = predictAndUpdateState(net,Xtest(i,:)');
end
% 预测自身
numTimeStepsSelf = 10;
Xself = Xtest(end,:);
Yself = zeros(numTimeStepsSelf,numResponses);
for i = 1:numTimeStepsSelf
[net,Yself(i,:)] = predictAndUpdateState(net,Xself');
Xself = Yself(i,:);
end
% 绘制预测结果
figure
plot(Ytest)
hold on
plot(YPredicted)
plot([numTimeStepsTest+1:numTimeStepsTest+numTimeStepsSelf],Yself)
legend('真实值','单步预测','自我预测')
```
需要注意的是,这只是一个简单的示例代码,你需要根据你的具体需求进行修改。此外,还需要准备好数据集,包括训练集和测试集。
相关推荐
![m](https://img-home.csdnimg.cn/images/20210720083646.png)
![m](https://img-home.csdnimg.cn/images/20210720083646.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)