AICc

get_oos_results

get_model_matrix

internal AICc function for lasso models
Internal function for obtaining oos results
Internal function for converting time series into model matrix of lags

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

AICc function

<dl><dt>fit</dt>
<dd>an object with logLik method,</dd>
<dt>eps</dt>
<dd>minimum df used in computation</dd>
<dt>fits</dt>
<dd>a list of fits with different tuning parameters</dd>
<dt>ytest</dt>
<dd>validation data</dd>
<dt>Xtest</dt>
<dd>new X data, including lags</dd>
<dt>y</dt>
<dd>time series vector</dd>
<dt>X</dt>
<dd>Additional exogenous features</dd>
<dt>n_lags_max</dt>
<dd>Maximum number of lags to add</dd></dl>

Arguments

internal AICc function for lasso models — AICc

<dl>

<dt>fit</dt>
<dd>an object with logLik method,</dd>


<dt>eps</dt>
<dd>minimum df used in computation</dd>


<dt>fits</dt>
<dd>a list of fits with different tuning parameters</dd>


<dt>ytest</dt>
<dd>validation data</dd>


<dt>Xtest</dt>
<dd>new X data, including lags</dd>


<dt>y</dt>
<dd>time series vector</dd>


<dt>X</dt>
<dd>Additional exogenous features</dd>


<dt>n_lags_max</dt>
<dd>Maximum number of lags to add</dd>

</dl>

AICc: internal AICc function for lasso models

Description

Usage

Arguments