

 
Manual Reference Pages  PDL::LINPRED (3)
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NAME
PDL::Filter::LinPred  Linear predictive filtering
CONTENTS
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(t1) and f(t2)
etc., but with LagsBehind => 2, f(t) is predicted from f(t2) and f(t3)).

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 <B>DataB>, you can just give the temporal autocorrelation
function.

Smooth

Don’t do prediction or filtering but smoothing.


The method <B>predictB> 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 v5.20.3  LINPRED (3)  20150812 
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