cnn-lstm 时间序列预测代码matlab
时间: 2023-10-02 14:09:38 浏览: 45
以下是一个简单的 CNN-LSTM 时间序列预测代码的 MATLAB 实现:
```matlab
% 加载数据
data = load('data.txt');
X = data(:, 1:end-1);
Y = data(:, end);
% 划分训练集和测试集
train_ratio = 0.8;
train_size = round(size(X, 1) * train_ratio);
train_X = X(1:train_size, :);
train_Y = Y(1:train_size, :);
test_X = X(train_size+1:end, :);
test_Y = Y(train_size+1:end, :);
% 对输入数据进行归一化处理
X_mean = mean(train_X);
X_std = std(train_X);
train_X = (train_X - X_mean) ./ X_std;
test_X = (test_X - X_mean) ./ X_std;
% 构建 CNN-LSTM 模型
input_size = size(X, 2);
num_filters = 64;
filter_size = 3;
num_lstm_units = 32;
layers = [
sequenceInputLayer(input_size)
convolution1dLayer(filter_size, num_filters, 'Padding', 'same')
reluLayer
sequenceFoldingLayer('Time','embedding')
lstmLayer(num_lstm_units, 'OutputMode', 'last')
fullyConnectedLayer(1)
regressionLayer];
% 设置训练选项
options = trainingOptions('adam', ...
'MaxEpochs', 100, ...
'MiniBatchSize', 64, ...
'ValidationData', {test_X, test_Y}, ...
'ValidationFrequency', 5, ...
'Verbose', false);
% 训练模型
net = trainNetwork(train_X, train_Y, layers, options);
% 预测未来值
future_X = [test_X(end, 2:end), 0]; % 最后一个时间点的特征值
future_X = (future_X - X_mean) ./ X_std;
future_Y = predict(net, future_X);
% 绘制预测结果
figure;
plot(1:size(test_Y, 1), test_Y, 'b');
hold on;
plot(size(test_Y, 1)+1, future_Y, 'r*');
title('Time Series Prediction');
legend('True Values', 'Predicted Future Value');
```
这个代码实现了一个简单的 CNN-LSTM 模型,使用 Adam 优化器进行训练,预测未来值并绘制预测结果。注意,这只是一个简单的示例代码,实际上要根据数据集的特点进行适当的调整。