stlar

STL decomposition method applied to the time series, then an AR model is used to
forecast seasonally adjusted data, while the seasonal naive method is used to forecast
the seasonal component

A novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. 'seer' package is the implementation of the FFORMS algorithm. For more details see our paper at <https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.

Thiyanga Talagala

seer

Feature-Based Forecast Model Selection

Rob J Hyndman

George Athanasopoulos

stlar function

<dl><dt>y</dt>
<dd>a univariate time series</dd>
<dt>h</dt>
<dd>forecast horizon</dd>
<dt>s.window</dt>
<dd>Either the character string “periodic” or the span (in lags) of the loess window for seasonal extraction</dd>
<dt>robust</dt>
<dd>logical indicating if robust fitting be used in the loess procedue</dd></dl>

Arguments

Author

STL-AR method — stlar

<dl>

<dt>y</dt>
<dd>a univariate time series</dd>


<dt>h</dt>
<dd>forecast horizon</dd>


<dt>s.window</dt>
<dd>Either the character string “periodic” or the span (in lags) of the loess window for seasonal extraction</dd>


<dt>robust</dt>
<dd>logical indicating if robust fitting be used in the loess procedue</dd>

</dl>

stlar: STL-AR method

Description

Usage

Value

Arguments

Author