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TIMESERIES REGRESSION AND PREDICTION CATEGORIES
Posted:
Mar 13, 2013 7:52 PM
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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
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