LinPred(3pm)
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.