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Thread Subject:
TIMESERIES REGRESSION AND PREDICTION CATEGORIES

Subject: TIMESERIES REGRESSION AND PREDICTION CATEGORIES

From: Greg Heath

Date: 13 Mar, 2013 23:52:12

Message: 1 of 2

Some of the time series posts indicate that there is some confusion regarding the
purpose, domain and overlap of the three basic timeseries functions. With the hope of reducing that confusion, I have posted posted some of my notes below:

% TIMESERIES REGRESSION AND PREDICTION CATEGORIES

net = narxnet( ID, FD, H ); % The most general openloop timeseries net.

ID Empty Row vector or Row vector of increasing NONNEGATIVE integers
FD Empty Row vector or Row vector of increasing POSITIVE integers
H Empty Row vector or Row vector of POSITIVE integers

ID Values of input delays
FD Values of output feedback delays
H Number of nodes per hidden layer
     
    y(t) = f( x( t - id : t ), y( t - fd : t - 1 ), H ), id >= 0, fd >=1
    
SPECIAL CASES
    
   1. Regression: min(ID) = 0
   
   2. Prediction: min(ID) > 0
   
    3. LINEAR: H = []
    
    4. TIMEDELAYNET: FD =[]
    
        net = narxnet( ID, [], H);
        
               = timedelaynet( ID, H );
               
        y(t) = f( x( t - id : t ) , H )
   
   5. NARNET: ID = []
   
        net = narxnet( [], FD, H );
        
              = narnet( FD, H );
              
       y(t) = f( y( t - fd : t - 1 ), H )

Hope this helps.

Greg
      
 

Subject: TIMESERIES REGRESSION AND PREDICTION CATEGORIES

From: Greg Heath

Date: 19 Sep, 2013 09:46:06

Message: 2 of 2

"Greg Heath" <heath@alumni.brown.edu> wrote in message <khr3fc$mmk$1@newscl01ah.mathworks.com>...
> Some of the time series posts indicate that there is some confusion regarding the
> purpose, domain and overlap of the three basic timeseries functions. With the hope of reducing that confusion, I have posted posted some of my notes below:
>
> % TIMESERIES REGRESSION AND PREDICTION CATEGORIES
>
> net = narxnet( ID, FD, H ); % The most general openloop timeseries net.
>
> ID Empty Row vector or Row vector of increasing NONNEGATIVE integers
> FD Empty Row vector or Row vector of increasing POSITIVE integers
> H Empty Row vector or Row vector of POSITIVE integers
>
> ID Values of input delays
> FD Values of output feedback delays
> H Number of nodes per hidden layer
>
> y(t) = f( x( t - id : t ), y( t - fd : t - 1 ), H ), id >= 0, fd >=1
>
> SPECIAL CASES
>
> 1. Regression: min(ID) = 0
>
> 2. Prediction: min(ID) > 0
>
> 3. LINEAR: H = []
>
> 4. TIMEDELAYNET: FD =[]
>
> net = narxnet( ID, [], H);
>
> = timedelaynet( ID, H );
>
> y(t) = f( x( t - id : t ) , H )
>
> 5. NARNET: ID = []
>
> net = narxnet( [], FD, H );
>
> = narnet( FD, H );
>
> y(t) = f( y( t - fd : t - 1 ), H )

Although, it makes sense that TIMEDELAYNET and NARNET should be available
as special cases of NARXNET, the current MATLAB implementation does not allow those special cases.

BUMMER!

Sorry, for the misinformation.

Greg

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