NAME
Algorithm::Burg - extrapolate time series using Burg's method
VERSION
version 0.001
SYNOPSIS
DESCRIPTION
The Algorithm::Burg module uses the Burg method to fit an
autoregressive (AR) model to the input data by minimizing (least
squares) the forward and backward prediction errors while constraining
the AR parameters to satisfy the Levinson-Durbin recursion.
DISCLAIMER: This is work in progress! The code is buggy and the
interface is subject to change.
ATTRIBUTES
coefficients
AR model polynomial coefficients computed by the train method.
order
AR model order
series_tail
Store the last "order" terms of the time series for "predict($n)".
METHODS
train($time_series)
Computes vector of coefficients using Burg algorithm applied to the
input source data $time_series.
predict($n)
Predict $n next values for the time series. If $n is 0 or bigger than
"order", assume $n = "order".
#!/usr/bin/env perl;
use strict;
use warnings qw(all);
use Algorithm::Burg;
...;
my $burg = Algorithm::Burg->new(order => 150);
$burg->train(\@time_series);
my $result = $burg->predict();
REFERENCES
* Burg's Method, Algorithm and Recursion
* C++ implementation
* Matlab/Octave implementation
* Python implementation
AUTHOR
Stanislaw Pusep
COPYRIGHT AND LICENSE
This software is copyright (c) 2014 by Stanislaw Pusep.
This is free software; you can redistribute it and/or modify it under
the same terms as the Perl 5 programming language system itself.