Maggie J. Xiong > PDL-Stats-0.6.2 > PDL::Stats::TS

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NAME ^

PDL::Stats::TS -- basic time series functions

DESCRIPTION ^

The terms FUNCTIONS and METHODS are arbitrarily used to refer to methods that are threadable and methods that are NOT threadable, respectively. Plots require PDL::Graphics::PGPLOT.

***EXPERIMENTAL!*** In particular, bad value support is spotty and may be shaky. USE WITH DISCRETION!

SYNOPSIS ^

    use PDL::LiteF;
    use PDL::NiceSlice;
    use PDL::Stats::TS;

    my $r = $data->acf(5);

acf

  Signature: (x(t); int h(); [o]r(h+1))

Autocorrelation function for up to lag h. If h is not specified it's set to t-1 by default.

acf does not process bad values.

usage:

    perldl> $a = sequence 10

    # lags 0 .. 5

    perldl> p $a->acf(5)
    [1 0.7 0.41212121 0.14848485 -0.078787879 -0.25757576]

acvf

  Signature: (x(t); int h(); [o]v(h+1))

Autocovariance function for up to lag h. If h is not specified it's set to t-1 by default.

acvf does not process bad values.

usage:

    perldl> $a = sequence 10

    # lags 0 .. 5

    perldl> p $a->acvf(5)
    [82.5 57.75 34 12.25 -6.5 -21.25]

    # autocorrelation
    
    perldl> p $a->acvf(5) / $a->acvf(0)
    [1 0.7 0.41212121 0.14848485 -0.078787879 -0.25757576]

Differencing. DX(t) = X(t) - X(t-1), DX(0) = X(0). Can be done inplace.

Integration. Opposite of differencing. IX(t) = X(t) + X(t-1), IX(0) = X(0). Can be done inplace.

Deseasonalize data using moving average filter the size of period d.

fill_ma

  Signature: (x(t); int q(); [o]xf(t))

Fill missing value with moving average. xf(t) = sum(x(t-q .. t-1, t+1 .. t+q)) / 2q.

fill_ma does handle bad values. Output pdl bad flag is cleared unless the specified window size q is too small and there are still bad values.

  my $x_filled = $x->fill_ma( $q );

Filter, exponential smoothing. xf(t) = a * x(t) + (1-a) * xf(t-1)

Filter, moving average. xf(t) = sum(x(t-q .. t+q)) / (2q + 1)

Mean absolute error. MAE = 1/n * sum( abs(y - y_pred) )

Usage:

    $mae = $y->mae( $y_pred );

Mean absolute percent error. MAPE = 1/n * sum(abs((y - y_pred) / y))

Usage:

    $mape = $y->mape( $y_pred );

Weighted mean absolute percent error. avg(abs(error)) / avg(abs(data)). Much more robust compared to mape with division by zero error (cf. Schütz, W., & Kolassa, 2006).

Usage:

    $wmape = $y->wmape( $y_pred );

Portmanteau significance test (Ljung-Box) for autocorrelations.

Usage:

    perldl> $a = sequence 10

    # acf for lags 0-5
    # lag 0 excluded from portmanteau
    
    perldl> p $chisq = $a->acf(5)->portmanteau( $a->nelem )
    11.1753902662994
   
    # get p-value from chisq distr

    perldl> use PDL::GSL::CDF
    perldl> p 1 - gsl_cdf_chisq_P( $chisq, 5 )
    0.0480112934306748

pred_ar

  Signature: (x(d); b(p|p+1); int t(); [o]pred(t))

Calculates predicted values up to period t (extend current series up to period t) for autoregressive series, with or without constant. If there is constant, it is the last element in b, as would be returned by ols or ols_t.

pred_ar does not process bad values.

  CONST  => 1,

Usage:

    perldl> $x = sequence 2

      # last element is constant
    perldl> $b = pdl(.8, -.2, .3)

    perldl> p $x->pred_ar($b, 7)
    [0       1     1.1    0.74   0.492  0.3656 0.31408]
 
      # no constant
    perldl> p $x->pred_ar($b(0:1), 7, {const=>0})
    [0       1     0.8    0.44   0.192  0.0656 0.01408]

season_m

Given length of season, returns seasonal mean and var for each period (returns seasonal mean only in scalar context).

Default options (case insensitive):

    START_POSITION => 0,     # series starts at this position in season
    MISSING        => -999,  # internal mark for missing points in season
    PLOT  => 1,              # boolean
      # see PDL::Graphics::PGPLOT::Window for next options
    WIN   => undef,          # pass pgwin object for more plotting control
    DEV   => '/xs',          # open and close dev for plotting if no WIN
                             # defaults to '/png' in Windows
    COLOR => 1,

See PDL::Graphics::PGPLOT for detailed graphing options.

    my ($m, $ms) = $data->season_m( 24, { START_POSITION=>2 } );

plot_dseason

Plots deseasonalized data and original data points. Opens and closes default window for plotting unless a pgwin object is passed in options. Returns deseasonalized data.

Default options (case insensitive):

    WIN   => undef,
    DEV   => '/xs',    # open and close dev for plotting if no WIN
                       # defaults to '/png' in Windows
    COLOR => 1,        # data point color

See PDL::Graphics::PGPLOT for detailed graphing options.

METHODS ^

plot_acf

Plots and returns autocorrelations for a time series.

Default options (case insensitive):

    SIG  => 0.05,      # can specify .10, .05, .01, or .001
    DEV  => '/xs',     # open and close dev for plotting
                       # defaults to '/png' in Windows

Usage:

    perldl> $a = sequence 10
    
    perldl> p $r = $a->plot_acf(5)
    [1 0.7 0.41212121 0.14848485 -0.078787879 -0.25757576]

REFERENCES ^

Brockwell, P.J., & Davis, R.A. (2002). Introcution to Time Series and Forecasting (2nd ed.). New York, NY: Springer.

Schütz, W., & Kolassa, S. (2006). Foresight: advantages of the MAD/Mean ratio over the MAPE. Retrieved Jan 28, 2010, from http://www.saf-ag.com/226+M5965d28cd19.html

AUTHOR ^

Copyright (C) 2009 Maggie J. Xiong <maggiexyz users.sourceforge.net>

All rights reserved. There is no warranty. You are allowed to redistribute this software / documentation as described in the file COPYING in the PDL distribution.

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