lstm时间序列预测matlab代码
时间: 2023-09-30 11:02:04 浏览: 80
下面是一个用MATLAB实现LSTM时间序列预测的示例代码:
```matlab
% 导入数据
data = csvread('data.csv'); % 假设数据存储在data.csv文件中
% 划分训练集和测试集
train_ratio = 0.8; % 训练集占总数据集的比例
train_size = floor(train_ratio * size(data, 1));
train_data = data(1:train_size, :);
test_data = data(train_size+1:end, :);
% 构造训练集和测试集的X和Y
lookback = 5; % 前5个时间步作为输入
trainX = zeros(train_size - lookback, lookback, size(train_data, 2));
trainY = train_data(lookback+1:end, :);
for i = 1:train_size-lookback
trainX(i, :, :) = train_data(i:i+lookback-1, :);
end
testX = zeros(size(test_data, 1) - lookback, lookback, size(test_data, 2));
testY = test_data(lookback+1:end, :);
for i = 1:size(test_data, 1)-lookback
testX(i, :, :) = test_data(i:i+lookback-1, :);
end
% 配置LSTM网络
num_features = size(train_data, 2);
num_hidden_units = 50;
num_epochs = 100;
batch_size = 32;
layers = [ ...
sequenceInputLayer(num_features)
lstmLayer(num_hidden_units, 'OutputMode', 'last')
fullyConnectedLayer(num_features)
regressionLayer];
% 训练LSTM网络
options = trainingOptions('adam', ...
'MaxEpochs', num_epochs, ...
'MiniBatchSize', batch_size, ...
'Verbose', false);
net = trainNetwork(trainX, trainY, layers, options);
% 预测测试集
predictedY = predict(net, testX);
% 可视化预测结果
figure
plot(testY)
hold on
plot(predictedY)
legend('True', 'Predicted')
xlabel('Time')
ylabel('Value')
title('LSTM Time Series Prediction')
```
其中,`data.csv`是存储时间序列数据的CSV文件,每一行是一个时间步,每一列是一个特征。`lookback`是指前几个时间步作为输入,`num_hidden_units`是LSTM层的隐藏单元数,`num_epochs`是训练轮数,`batch_size`是每次训练的样本数。`trainX`、`trainY`、`testX`、`testY`是训练集和测试集的输入和输出,`layers`是LSTM网络的层次结构,`options`是训练参数。最后,使用`predict`函数预测测试集并可视化预测结果。
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