请写出deepar时间序列概率预测的matlab代码

以下是使用MATLAB实现DeepAR时间序列概率预测的代码示例：

% 准备数据集
data = readtable('data.csv');
timestamps = datetime(data.Timestamp);
target = data.Target;

% 数据预处理
target(target < 0) = NaN;
target = fillmissing(target, 'linear');
target = normalize(target);

% 数据处理和转换
context_feat = data.ContextFeat;
time_feat = data.TimeFeat;
time_feat = normalize(time_feat);

% 构建DeepAR模型
num_epochs = 100;
mini_batch_size = 32;
learning_rate = 0.001;
dropout_rate = 0.1;
prediction_length = 24;
cardinality = [1];
embedding_dimension = [10];
num_cells = 32;
num_layers = 2;

input_layer = sequenceInputLayer(1, 'Name', 'Feature');
lstm_layer = lstmLayer(num_cells, 'OutputMode', 'last', 'Name', 'LSTM');
output_layer = fullyConnectedLayer(1, 'Name', 'FC');
network = layerGraph();
network = addLayers(network, input_layer);
network = addLayers(network, lstm_layer);
network = addLayers(network, output_layer);
network = connectLayers(network, 'input', 'LSTM');
network = connectLayers(network, 'LSTM', 'FC');
net = trainNetwork(XTrain,YTrain,layers,options);

% 训练模型
XTrain = cat(3, target', context_feat', time_feat');
YTrain = target(p + 1:end)';
options = trainingOptions('adam', ...
    'MaxEpochs',num_epochs, ...
    'MiniBatchSize',mini_batch_size, ...
    'GradientThreshold',1, ...
    'InitialLearnRate',learning_rate, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropFactor',0.1, ...
    'LearnRateDropPeriod',50, ...
    'L2Regularization',0.01, ...
    'ValidationData',{XTest,YTest}, ...
    'ValidationFrequency',24, ...
    'Verbose',0, ...
    'Plots','training-progress');

% 评估模型
XTest = cat(3, target(end-p+1:end)', context_feat(end-p+1:end)', time_feat(end-p+1:end)');
YTest = target(end-p+1:end)';
YPred = predict(net,XTest,'MiniBatchSize',1,'SequenceLength','longest');

% 预测未来值
future_time = timestamps(end) + hours(1:prediction_length);
future_context_feat = randi([0,1], prediction_length, 1);
future_time_feat = repmat(time_feat(end, :), prediction_length, 1);
future_data = table(future_time, future_context_feat, future_time_feat);
future_target = zeros(prediction_length, 1);
for i = 1:prediction_length
    XNew = cat(3, future_target(1:i-1)', future_context_feat(1:i-1)', future_time_feat(1:i-1)');
    YNew = predict(net, XNew, 'MiniBatchSize', 1, 'SequenceLength', 'longest');
    future_target(i) = YNew(end);
end
future_target = denormalize(future_target);

这段代码仅作为示例，具体实现需要根据实际情况进行调整。同时，需要注意的是，DeepAR模型的训练和预测需要大量的计算资源，需要根据实际情况选择合适的硬件和参数配置。