【MATLAB第55期】#源码分享| 基于MATLAB的LSTM长短期记忆网络的一列数据时间序列预测模型-阿里云开发者社区

【MATLAB第55期】#源码分享| 基于MATLAB的LSTM长短期记忆网络的一列数据时间序列预测模型

此示例说明如何使用长短期记忆(LSTM)网络预测时间序列数据。
为了预测序列的未来时间步长的值，可以训练一个序列到序列回归LSTM网络，其中的响应值移动了一个时间步长的训练序列。也就是说，在输入序列的每个时间步长，LSTM网络学习预测下一个时间步长的值。
要预测未来多个时间点的值，请使用forectAndUpdateState函数一次预测一个时间点，并在每次预测时更新网络状态。

一、加载数据

加载示例数据。某股票的收盘价，包含单个时间序列，时间步长为每日，值对应于每日收盘价。
输出是一个单元数组，其中每个元素都是单个时间步长。将数据重塑为行矢量。

% 导入数据
clear,clc
data = importdata('out.txt');
data = data';
figure()
plot(data,LineWidth=2)

二、数据拆分

划分训练和测试数据。在序列的前90%进行训练，在最后10%进行测试。

% 定义测试与训练长度
numTimeStepsTrain = floor(0.9*numel(data));
dataTrain = data(1:numTimeStepsTrain+1);
dataTest = data(numTimeStepsTrain+1:end);

三、数据归一化

为了更好地匹配和防止训练发散，将训练数据标准化为零均值和单位方差。
在预测时，您必须使用与训练数据相同的参数来标准化测试数据。

% 训练数据归一化
mu = mean(dataTrain);
sig = std(dataTrain);
dataTrainStandardized = (dataTrain - mu) / sig;

要预测序列的未来时间步长的值，请将响应指定为值移位一个时间步长的训练序列。也就是说，在输入序列的每个时间步长，LSTM网络学习预测下一个时间步长的值。

XTrain = dataTrainStandardized(1:end-1);
YTrain = dataTrainStandardized(2:end);

四、定义网络结构

创建LSTM回归网络。
将LSTM层指定为具有128个隐藏单元。
训练参数可查看帮助，明白其中含义。

% 定义网络结构
layers = [
    sequenceInputLayer(1,"Name","input")
    lstmLayer(128,"Name","lstm")
%     dropoutLayer(0.2,"Name","drop")
    fullyConnectedLayer(1,"Name","fc")
    regressionLayer];
% 定义训练参数
options = trainingOptions('adam', ...
    'MaxEpochs',250, ...
    'GradientThreshold',1, ...
    'InitialLearnRate',0.005, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropPeriod',125, ...
    'LearnRateDropFactor',0.2, ...
    'Verbose',0, ...
    'Plots','training-progress');

五、训练网络

使用TrainNetwork训练LSTM网络。

% 训练网络
net = trainNetwork(XTrain,YTrain,layers,options);

六、预测

1.使用预测值更新网络状态

要预测未来多个时间点的值，请使用forectAndUpdateState函数一次预测一个时间点，并在每次预测时更新网络状态。
对于每个预测，使用先前的预测作为函数的输入。使用与训练数据相同的参数对测试数据进行标准化。

% 测试集归一化
dataTestStandardized = (dataTest - mu) / sig;
XTest = dataTestStandardized(1:end-1);
% 预测
net = predictAndUpdateState(net,XTrain);
[net,YPred] = predictAndUpdateState(net,YTrain(end));

numTimeStepsTest = numel(XTest);
for i = 2:numTimeStepsTest
    [net,YPred(:,i)] = predictAndUpdateState(net,YPred(:,i-1),'ExecutionEnvironment','cpu');
end

为了初始化网络状态，首先对训练数据XTrain进行预测。
接下来，使用训练响应YTrain的最后一个时间步长进行第一个预测(完)。
循环其余的预测，并将先前的预测输入到forectAndUpdateState。
对于大型数据集合、长序列或大型网络，在GPU上计算预测通常比在CPU上计算预测快。否则，在CPU上进行预测的计算速度通常会更快。对于单时间步长预测，请使用CPU。要使用CPU进行预测，请将recrectAndUpdateState的‘ExecutionEnvironment’选项设置为‘CPU’
使用先前计算的参数来取消预测的标准化。

YPred = sig*YPred + mu;
用预测值绘制训练时间序列。

% 绘图
figure
plot(dataTrain(1:end-1))
hold on
idx = numTimeStepsTrain:(numTimeStepsTrain+numTimeStepsTest);
plot(idx,[data(numTimeStepsTrain) YPred],'k.-'),hold on
plot(idx,data(numTimeStepsTrain:end-1),'r'),hold on
hold off
xlabel("Day")
ylabel("P")
title("Forecast")
legend(["Observed" "Forecast"])

2.使用测试数据更新网络状态

如果您可以访问预测之间时间步长的实际值，则可以使用观测值而不是预测值来更新网络状态。
首先，初始化网络状态。要对新序列进行预测，请使用Reset State重置网络状态。重置网络状态可防止先前的预测影响对新数据的预测。
重置网络状态，然后通过对训练数据进行预测来初始化网络状态。

net = resetState(net);
net = predictAndUpdateState(net,XTrain);

之后预测与前面基本一样，只不过用XTest中的数据更新网络状态

% 重置网络状态
net = resetState(net);
net = predictAndUpdateState(net,XTrain);
%

YPred = [];
numTimeStepsTest = numel(XTest);
for i = 1:numTimeStepsTest
    [net,YPred(:,i)] = predictAndUpdateState(net,XTest(:,i),'ExecutionEnvironment','cpu');
end

YPred = sig*YPred + mu;

% 绘图
figure
plot(dataTrain(1:end-1))
hold on
idx = numTimeStepsTrain:(numTimeStepsTrain+numTimeStepsTest);
plot(idx,[data(numTimeStepsTrain) YPred],'k.-'),hold on
plot(idx,data(numTimeStepsTrain:end-1),'r'),hold on
hold off
xlabel("Day")
ylabel("P")
title("Forecast")
legend(["Observed" "Forecast"])

七、数据

数据重命名为out.txt

1.885999999999999899e+00
1.883000000000000007e+00
1.877999999999999892e+00
1.893999999999999906e+00
1.872999999999999998e+00
1.893000000000000016e+00
1.893000000000000016e+00
1.909999999999999920e+00
1.903999999999999915e+00
1.893000000000000016e+00
1.887000000000000011e+00
1.891999999999999904e+00
1.905000000000000027e+00
1.875999999999999890e+00
1.879999999999999893e+00
1.832000000000000073e+00
1.709000000000000075e+00
1.745000000000000107e+00
1.762999999999999901e+00
1.794000000000000039e+00
1.792000000000000037e+00
1.800999999999999934e+00
1.816000000000000059e+00
1.830999999999999961e+00
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1.832999999999999963e+00
1.872999999999999998e+00
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1.866000000000000103e+00
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1.903000000000000025e+00
1.901000000000000023e+00
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1.830000000000000071e+00
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1.899000000000000021e+00
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1.923000000000000043e+00
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1.883000000000000007e+00
1.854000000000000092e+00
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1.762999999999999901e+00
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1.711999999999999966e+00
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1.752999999999999892e+00
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1.741999999999999993e+00
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1.784000000000000030e+00
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1.812999999999999945e+00
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1.814999999999999947e+00
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1.824000000000000066e+00
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2.259900000000000020e+00

【MATLAB第55期】#源码分享| 基于MATLAB的LSTM长短期记忆网络的一列数据时间序列预测模型

【MATLAB第55期】#源码分享| 基于MATLAB的LSTM长短期记忆网络的一列数据时间序列预测模型

一、加载数据

二、数据拆分

三、数据归一化

四、定义网络结构

五、训练网络

六、预测

1.使用预测值更新网络状态

2.使用测试数据更新网络状态

七、数据

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【MATLAB第55期】#源码分享| 基于MATLAB的LSTM长短期记忆网络的一列数据时间序列预测模型

【MATLAB第55期】#源码分享| 基于MATLAB的LSTM长短期记忆网络的一列数据时间序列预测模型

一、加载数据

二、 数据拆分

三、数据归一化

四、定义网络结构

五、训练网络

六、预测

1.使用预测值更新网络状态

2.使用测试数据更新网络状态

七、数据

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