04-18-2008
Authorizing Access to Dynamic Spatial-Temporal Data
Authorizing access to individual data objects based on spatial and temporal references is a complex task. Read this case study to learn one feasible approach.
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LEARN ABOUT DEBIAN
pdl::linpred
LinPred(3pm) User Contributed Perl Documentation 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.
perl v5.14.2 2011-03-30 LinPred(3pm)