% 股价预测 - LSTM-Adaboost集成学习

% 加载股价数据
load(‘stock_data.mat’); % 假设数据已经存储在stock_data.mat文件中
prices = stock_data.prices; % 假设股价数据存储在名为prices的变量中

% 数据预处理
normalized_prices = (prices - mean(prices)) / std(prices); % 数据归一化

% 划分训练集和测试集
train_ratio = 0.8; % 训练集比例
train_size = floor(train_ratio * length(normalized_prices));
train_data = normalized_prices(1:train_size);
test_data = normalized_prices(train_size+1:end);

% 参数设置
sequence_length = 10; % 序列长度
num_epochs = 50; % 迭代次数
num_hidden_units = 50; % LSTM隐藏单元数量

% LSTM训练
X_train = []; % LSTM训练集
y_train = []; % LSTM目标值

% 构建LSTM训练集和目标值
for i = 1:length(train_data)-sequence_length
X_train = [X_train; train_data(i:i+sequence_length-1)];
y_train = [y_train; train_data(i+sequence_length)];
end

% LSTM模型训练
input_size = 1; % 输入维度
output_size = 1; % 输出维度
layers = [ …
sequenceInputLayer(input_size)
bilstmLayer(num_hidden_units, ‘OutputMode’, ‘sequence’)
fullyConnectedLayer(output_size)
regressionLayer];
options = trainingOptions(‘adam’, ‘MaxEpochs’, num_epochs);
lstm_model = trainNetwork(X_train’, y_train’, layers, options);

% LSTM预测
X_test = []; % LSTM测试集

% 构建LSTM测试集
for i = 1:length(test_data)-sequence_length
X_test = [X_test; test_data(i:i+sequence_length-1)];
end

lstm_predictions = predict(lstm_model, X_test’);

% Adaboost集成学习
T = 10; % 迭代次数
alpha = zeros(T, 1); % 权重
models = cell(T, 1); % LSTM模型集合
predictions = zeros(length(lstm_predictions), T); % 每个模型的预测结果

% 初始化权重
w = ones(length(lstm_predictions), 1) / length(lstm_predictions);

for t = 1:T
% 使用当前权重训练LSTM模型
model = trainNetwork(X_train’, y_train’, layers, options, ‘InitialLearnRate’, 0.1, ‘LearnRateSchedule’, ‘piecewise’, ‘LearnRateDropFactor’, 0.1, ‘LearnRateDropPeriod’, 20);
models{t} = model;

% 在测试集上进行预测
predictions(:, t) = predict(model, X_test');% 计算误差
errors = abs(predictions(:, t) - lstm_predictions);% 计算加权误差
weighted_error = sum(w .* errors);% 计算模型权重
alpha(t) = 0.5 * log((1 - weighted_error) / weighted_error);% 更新权重
w = w .* exp(-alpha(t) * errors);
w = w / sum(w);

end

% 集成模型预测
ensemble_predictions = sign(sum(repmat(alpha, 1, size(predictions, 1)) .* predictions’, 1));

% 绘制预测结果
time = 1:length(test_data);
figure;
plot(time, test_data, ‘b-’, ‘LineWidth’, 1.5);
hold on;
plot(time, ensemble_predictions, ‘r–’, ‘LineWidth’, 1.5);
xlabel(‘时间’);
ylabel(‘股价’);
title(‘股价预测’);
legend(‘实际股价’, ‘预测股价’);
grid on;