penalty_scaler

Penalty Scaling Function for parametric penalty weights

An implementation of sparsity-ranked lasso and related methods
for time series data. This methodology is especially useful for
large time series with exogenous features and/or complex
seasonality. Originally described in Peterson and Cavanaugh
(2022) <doi:10.1007/s10182-021-00431-7> in the context of variable
selection with interactions and/or polynomials, ranked sparsity is
a philosophy with methods useful for variable selection in the
presence of prior informational asymmetry. This situation exists for time
series data with complex seasonality, as shown in Peterson and Cavanaugh
(2024) <doi:10.1177/1471082X231225307>, which also describes this package
in greater detail. The sparsity-ranked penalization methods for time series
implemented in 'fastTS' can fit large/complex/high-frequency time series
quickly, even with a high-dimensional exogenous feature set. The method is
considerably faster than its competitors, while often producing more
accurate predictions. Also included is a long hourly series of arrivals
into the University of Iowa Emergency Department with concurrent local
temperature.

Ryan Andrew Peterson

fastTS

Fast Time Series Modeling for Seasonal Series with Exogenous
Variables

penalty_scaler function

<dl><dt>lag</dt>
<dd>a vector of lags for which to calculate the penalty function</dd>
<dt>m</dt>
<dd>a vector of seasonality modes</dd>
<dt>r</dt>
<dd>a vector of dim (m + 1) for the factor penalties on c(m, time)</dd>
<dt>plot</dt>
<dd>logical; whether to plot the penalty function</dd>
<dt>log</dt>
<dd>logical; whether to return the log of the penalty function</dd></dl>

Arguments

Penalty Scaling Function for parametric penalty weights — penalty_scaler

<dl>

<dt>lag</dt>
<dd>a vector of lags for which to calculate the penalty function</dd>


<dt>m</dt>
<dd>a vector of seasonality modes</dd>


<dt>r</dt>
<dd>a vector of dim (m + 1) for the factor penalties on c(m, time)</dd>


<dt>plot</dt>
<dd>logical; whether to plot the penalty function</dd>


<dt>log</dt>
<dd>logical; whether to return the log of the penalty function</dd>

</dl>

penalty_scaler: Penalty Scaling Function for parametric penalty weights

Description

Usage

Arguments