MATLAB: How to manually create a non-linear input-output time series neural network

narnetnarxnetneural networkrnn

Hey there.

I want to manually write and train a neural network, as aviable in the Neural Time Series Tool.

In the tool, three options are aviable:

1) Predict series y(t) giving d past values of y(t) and another series X(t)

2) Predict series y(t) giving d past values of y(t)

3) Predict series y(t) giving d past values of X(t)

I have found the syntax for the first and second options (narxnet and narnet), but not to the third one, which is the one I am after.

Could someone tell me what is the matlab syntax of the third option?

My goal is to automate the process of choosing the best train session (avoid local optimas) and the choosing of the hyper-parameters (find the best number of layers, find the best delay time, etc…)

By the way – I am using matlab 2015b, if it matters…

Best Answer

 help timedelaynet
 doc timedelaynet

Hope this helps

Thank you for formally accepting my answer

Greg

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MAPMINMAX can be inappropriate only if there are outliers.

Use MAPSTD to detect outliers that can be removed or modified.

Hope this helps.

Thank you for formally accepting my answer

Greg

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

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

Best Answer

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MATLAB: Normalize Inputs and Targets of neural network

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

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