NAME

SYNOPSIS

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

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

DESCRIPTION

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