Predicting one period ahead using neural networks

Question

Danielle Leblance el 6 de Dic. de 2017

0
Enlazar

Enlace directo a esta pregunta

https://la.mathworks.com/matlabcentral/answers/371332-predicting-one-period-ahead-using-neural-networks

Respondida: Greg Heath el 8 de Dic. de 2017

Data0.zip

Hi, I have prices and i am trying to predict next period's price using neural network. I found a code on Mathworks website and modified it (the data is attached). However, i am failing to predict one period ahead because the code is generating errors that are the differences between real data and the model data for the validation data. I am using neural networks for the first time and i don't know how to generate the forecast for the next period (last day in the sample is 28th july 2017 and i want to predict the price of 29th of July 2017) any help is greatly appreciated

close all, clear all, clc, format compact
load Data0
% data settings
N  = length(Price)*0.70; % number of samples
Nu = length(Price)*0.30; % number of learning samples
% prepare training data
yt = con2seq(Price(1:Nu)');
% prepare validation data
yv = con2seq(Price(Nu+1:end)');
inputDelays = 3;
hiddenSizes =2;
% nonlinear autoregressive neural network
net = narnet(inputDelays, hiddenSizes);
[Xs,Xi,Ai,Ts] = preparets(net,{},{},yt);
%%Prepare input and target time series data for network training
% [Xs,Xi,Ai,Ts,EWs,shift] = preparets(net,Xnf,Tnf,Tf,EW)
%
% This function simplifies the normally complex and error prone task of
% reformatting input and target timeseries. It automatically shifts input
% and target time series as many steps as are needed to fill the initial
% input and layer delay states. If the network has open loop feedback,
% then it copies feedback targets into the inputs as needed to define the
% open loop inputs.
%
%  net : Neural network
%  Xnf : Non-feedback inputs
%  Tnf : Non-feedback targets
%   Tf : Feedback targets
%   EW : Error weights (default = {1})
%
%   Xs : Shifted inputs
%   Xi : Initial input delay states
%   Ai : Initial layer delay states
%   Ts : Shifted targets
[Xs,Xi,Ai,Ts] = preparets(net,{},{},yt);
% train net with prepared training data
net = train(net,Xs,Ts,Xi,Ai);
% close feedback for recursive prediction
net = closeloop(net);
%%Recursive prediction on validation data
% prepare validation data for network simulation
yini = yt(end-max(inputDelays)+1:end); % initial values from training data
% combine initial values and validation data 'yv'
[Xs,Xi,Ai] = preparets(net,{},{},[yini yv]);
% predict on validation data
predict = net(Xs,Xi,Ai);
% validation data
Yv = cell2mat(yv);
% prediction
Yp = cell2mat(predict);
% error
e = Yv - Yp;