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.