基于lstm的房价波动预测的matlab代码

以下是一个基于LSTM的房价波动预测的Matlab代码示例：

% 导入数据
data = xlsread('house_price.xlsx');
train_data = data(1:300,:);
test_data = data(301:end,:);

% 归一化
min_train = min(train_data);
max_train = max(train_data);
train_data = (train_data - min_train) ./ (max_train - min_train);
test_data = (test_data - min_train) ./ (max_train - min_train);

% 构建训练集和标签
train_x = zeros(299,3,30);
train_y = zeros(299,1);
for i = 1:299
    train_x(i,:,:) = train_data(i:i+2,:)';
    train_y(i) = train_data(i+3,1);
end

% 构建 LSTM 模型
num_features = 3;
num_hidden_units = 50;
layers = [ ...
    sequenceInputLayer(num_features)
    lstmLayer(num_hidden_units,'OutputMode','last')
    fullyConnectedLayer(1)
    regressionLayer];

% 训练 LSTM 模型
options = trainingOptions('adam', ...
    'MaxEpochs',200, ...
    'GradientThreshold',1, ...
    'InitialLearnRate',0.005, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropFactor',0.5, ...
    'LearnRateDropPeriod',50, ...
    'Verbose',0, ...
    'Plots','training-progress');
net = trainNetwork(train_x,train_y,layers,options);

% 预测测试集
test_x = zeros(66,3,30);
for i = 1:66
    test_x(i,:,:) = test_data(i:i+2,:)';
end
test_y = predict(net,test_x);

% 反归一化
test_y = test_y .* (max_train(1) - min_train(1)) + min_train(1);
test_data = test_data .* (max_train - min_train) + min_train;

% 绘制结果
plot(test_data(:,1),'b');
hold on;
plot(test_y,'r');
legend('Actual','Predicted');
xlabel('Time');
ylabel('House Price');

这个代码示例中，我们首先导入数据，然后将数据归一化。接着，我们使用前30个房价数据来构建训练集和标签，每次预测后移一步，以此类推。我们使用这些序列来训练一个LSTM模型，并在测试集上进行预测。最后，我们反归一化预测结果并绘制出实际和预测的房价。