MATLAB: Input-Output Fitting problem with a Neural Network – Stock predication

fitnetnnstarttrain

Hi, I'm trying to predict stock values(in this case, the closing value). In the end I want to achieve the same result as this example, Example
I use Apples historical data over a year( historical data )
I will use the "close price" and its corresponding date, but I cannot figure out how to use "input" and "target" and what they mean.
    % This script assumes these variables are defined:
    x = simplefitInput; // 
    t = simplefitTargets;
    % 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);
   % Test the Network
   y = net(x);
   e = gsubtract(t,y);
   performance = perform(net,t,y)
   % View the Network
   %view(net)
If understand it correctly, x is the input of the system. But what is t ? So Im wondering: What kind of values do I give to x and t ?

Best Answer

close all, clear all, clc, plt = 0
% NOTE: SOME SEMICOLONS REMOVED FOR DISPLAY PURPOSES
 % Read Useful Info
 %    help nndatasets
 %    doc nndatasets
 % x is the input
 % t is the training target for the output
 % y is the output
 % e is the error (e=t-y)
% Data Selection
 [ x ,t ] = simplefit_dataset; 
 whos
 [I N ] = size(x)           %[ 1 94 ]

 [O N ] = size(t)            %[ 1 94 ]
%Read more Useful Info
 %  help simplefit_dataset
 %  doc simplefit_dataset
 plt = plt+1, figure(plt)
 plot(x,t,'LineWidth',2)
 MSE00 = var(t',1)          % 8.3378 MSE Reference
% Using as many defaults as is reasonable:
net = fitnet;                 % net = net will reveal all defaults
[ net tr y e] = train(net,x); % tr = tr will reveal  training details 
%    y = net(x);
%    e = t - y
 MSE  = mse(e)      %  4.9880e-008  Mean-squared-error
 NMSE = MSE/MSE00   %  5.9824e-009  Normalized
% Coefficient of determination, Rsquared, is interpreted as the fraction of target variance that is modeled by the net. See Wikipedia for a decent discussion
   R2       = 1-NMSE           %  1.0000   Almost Perfect!
%See the training record,tr, for details of the default data division into training, validation and test subsets and training details
 tr = tr
Hope this helps.
Thank you for formally accepting my answer
Greg
Related Question