一、資料集介紹

1.輸入資料（XRHJDataInput.mat）

開盤價（1）、成交量（2），共641行，2列，

2.目標資料（XRHJDataTarget.mat）

收盤價（1）、最高價（2）、最低價（3）,共641行，3列，

3.預測資料（newdata_pre18.mat）

開盤價（1）、成交量（2），共18行，2列，

資料集：https://pan.baidu.com/s/1JqCpsVGcRXHZS3tL31DREw
提取碼：TTXS

二、模型訓練

1.訓練程序

2.Matlab程式代碼

% Solve an Input-Output Fitting problem with a Neural Network
% Script generated by Neural Fitting app
% Created 07-Oct-2021 22:50:17
%
% This script assumes these variables are defined:
%
%   data - input data.
%   dataTarget - target data.
load XRHJDataInput.mat
load XRHJDataTarget.mat

x = data';
t = dataTarget';

% Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'trainlm';  % Levenberg-Marquardt backpropagation.

% Create a Fitting Network
hiddenLayerSize = 10;
net = fitnet(hiddenLayerSize,trainFcn);

% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

% Train the Network
[net,tr] = train(net,x,t);

% Test the Network
y = net(x);
e = gsubtract(t,y);
performance = perform(net,t,y)

% View the Network
view(net)

% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, ploterrhist(e)
%figure, plotregression(t,y)
%figure, plotfit(net,x,t)

%泛化
load newdata_pre18.mat
x = newdata';%newdata
BPOutPut=sim(net,x)%