ets_fit_impl

Low-Level Exponential Smoothing function for translating modeltime to forecast

internal

The time series forecasting framework for use with the 'tidymodels' ecosystem.
Models include ARIMA, Exponential Smoothing, and additional time series models
from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition"
(<https://otexts.com/fpp2/>).
Refer to "Prophet: forecasting at scale"
(<https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).

Matt Dancho

modeltime

The Tidymodels Extension for Time Series Modeling

Business Science 

ets_fit_impl function

<dl><dt>x</dt>
<dd>A dataframe of xreg (exogenous regressors)</dd>
<dt>y</dt>
<dd>A numeric vector of values to fit</dd>
<dt>period</dt>
<dd>A seasonal frequency. Uses "auto" by default. A character phrase
of "auto" or time-based phrase of "2 weeks" can be used if a date or date-time variable is provided.</dd>
<dt>error</dt>
<dd>The form of the error term: "auto", "additive", or "multiplicative".
If the error is multiplicative, the data must be non-negative.</dd>
<dt>trend</dt>
<dd>The form of the trend term: "auto", "additive", "multiplicative" or "none".</dd>
<dt>season</dt>
<dd>The form of the seasonal term: "auto", "additive", "multiplicative" or "none".</dd>
<dt>damping</dt>
<dd>Apply damping to a trend: "auto", "damped", or "none".</dd>
<dt>alpha</dt>
<dd>Value of alpha. If NULL, it is estimated.</dd>
<dt>beta</dt>
<dd>Value of beta. If NULL, it is estimated.</dd>
<dt>gamma</dt>
<dd>Value of gamma. If NULL, it is estimated.</dd>
<dt>...</dt>
<dd>Additional arguments passed to <code>forecast::ets</code></dd></dl>

Arguments

Low-Level Exponential Smoothing function for translating modeltime to forecast — ets_fit_impl

<dl>

<dt>x</dt>
<dd>A dataframe of xreg (exogenous regressors)</dd>


<dt>y</dt>
<dd>A numeric vector of values to fit</dd>


<dt>period</dt>
<dd>A seasonal frequency. Uses "auto" by default. A character phrase
of "auto" or time-based phrase of "2 weeks" can be used if a date or date-time variable is provided.</dd>


<dt>error</dt>
<dd>The form of the error term: "auto", "additive", or "multiplicative".
If the error is multiplicative, the data must be non-negative.</dd>


<dt>trend</dt>
<dd>The form of the trend term: "auto", "additive", "multiplicative" or "none".</dd>


<dt>season</dt>
<dd>The form of the seasonal term: "auto", "additive", "multiplicative" or "none".</dd>


<dt>damping</dt>
<dd>Apply damping to a trend: "auto", "damped", or "none".</dd>


<dt>alpha</dt>
<dd>Value of alpha. If NULL, it is estimated.</dd>


<dt>beta</dt>
<dd>Value of beta. If NULL, it is estimated.</dd>


<dt>gamma</dt>
<dd>Value of gamma. If NULL, it is estimated.</dd>


<dt>...</dt>
<dd>Additional arguments passed to <code>forecast::ets</code></dd>

</dl>

ets_fit_impl: Low-Level Exponential Smoothing function for translating modeltime to forecast

Description

Usage

Arguments