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Authorizing Access to Dynamic Spatial-Temporal Data

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pdl::linpred

LinPred(3)						User Contributed Perl Documentation						LinPred(3)

NAME

       PDL::Filter::LinPred - Linear predictive filtering

SYNOPSIS

	       $a = new PDL::Filter::LinPred(
		       {NLags => 10,
			LagInterval => 2,
			LagsBehind => 2,
			Data => $dat});

	       ($pd,$corrslic) = $a->predict($dat);

DESCRIPTION

       A filter by doing linear prediction: tries to predict the next value in a data stream as accurately as possible. The filtered data is the
       predicted value. The parameters are

       NLags   Number of time lags used for prediction

       LagInterval
	       How many points each lag should be

       LagsBehind
	       If, for some strange reason, you wish to predict not the next but the one after that (i.e. usually f(t) is predicted from f(t-1)
	       and f(t-2) etc., but with LagsBehind => 2, f(t) is predicted from f(t-2) and f(t-3)).

       Data    The input data, which may contain other dimensions past the first (time).  The extraneous dimensions are assumed to represent
	       epochs so the data is just concatenated.

       AutoCovar
	       As an alternative to Data, you can just give the temporal autocorrelation function.

       Smooth  Don't do prediction or filtering but smoothing.

       The method predict gives a prediction for some data plus a corresponding slice of the data, if evaluated in list context. This slice is
       given so that you may, if you wish, easily plot them atop each other.

       The rest of the documentation is under lazy evaluation.

AUTHOR

       Copyright (C) Tuomas J. Lukka 1997.  All rights reserved. There is no warranty. You are allowed to redistribute this software /
       documentation under certain conditions. For details, see the file COPYING in the PDL distribution. If this file is separated from the PDL
       distribution, the copyright notice should be included in the file.

perl v5.12.1							    2009-10-17								LinPred(3)