目录
💥1 概述
📚2 运行结果
🎉3 参考文献
👨💻4 Matlab代码
💥1 概述
LSTM模型的一个常见用途是对长时间序列数据进行学习预测，例如得到了某商品前一年的日销量数据，我们可以用LSTM模型来预测未来一段时间内该商品的销量。但对于不熟悉神经网络或者对没有了解过RNN模型的人来说，想要看懂LSTM模型的原理是非常困难的，但有些时候我们不得不快速上手搭建一个LSTM模型来完成预测任务。本文分析在线产品价格数据以预测当前产品价格。
首先建立模型，确定每个因素对定价的影响程度，并且能够预测出在不同变量组合下的价格；从而根据特定的价格水平，对产品进行设计，制定商业策略。
📚2 运行结果
🎉3 参考文献
[1]杨青,王晨蔚.基于深度学习LSTM神经网络的全球股票指数预测研究[J].统计研究,2019,36(03):65-77.DOI:10.19343/j.cnki.11-1302/c.2019.03.006.
👨💻4 Matlab代码
主函数部分代码：
jean_data = readtable('jean_sales.xlsx');
% Fill the NaN value with the Nearest value.
jean_data.sales_price = fillmissing(jean_data.sales_price, 'nearest');
lenofdata = length(jean_data.sales_price);
for i=1 : length(jean_data.collect_day)
jean_data.collect_day(i) = strip(jean_data.collect_day(i),"'");
end
Y = jean_data.sales_price;
data = Y';
numTimeStepsTrain = floor(0.9*numel(data));
dataTrain = data(1:numTimeStepsTrain+1);
dataTest = data(numTimeStepsTrain+1:end);
% Normalize sales_price to a value between 0 and 1 (Training Data Set)
mu = mean(dataTrain);
sig = std(dataTrain);
dataTrainStandardized = (dataTrain - mu) / sig;
XTrain = dataTrainStandardized(1:end-1);
YTrain = dataTrainStandardized(2:end);
%LSTM Net Architecture Def
numFeatures = 1;
numResponses = 1;
numHiddenUnits = 200;
layers = [ ...
sequenceInputLayer(numFeatures)
lstmLayer(numHiddenUnits)
fullyConnectedLayer(numResponses)
regressionLayer];
options = trainingOptions('adam', ...
'MaxEpochs',500, ...
'GradientThreshold',1, ...
'InitialLearnRate',0.005, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropPeriod',125, ...
'LearnRateDropFactor',0.2, ...
'Verbose',0, ...
'Plots','training-progress');
% Train LSTM Net
net = trainNetwork(XTrain,YTrain,layers,options);
% Normalize sales_price to a value between 0 and 1 (Testing Data Set)
dataTestStandardized = (dataTest - mu) / sig;
XTest = dataTestStandardized(1:end-1);
net = predictAndUpdateState(net,XTrain);
[net,YPred] = predictAndUpdateState(net,YTrain(end));
% Predict as long as the test period (2019.05.07 ~ 2019.10.31)
numTimeStepsTest = numel(XTest);
for i = 2:numTimeStepsTest
[net,YPred(:,i)] = predictAndUpdateState(net,YPred(:,i-1),'ExecutionEnvironment','cpu');
end
