split_names

split the names of ARIMA, ETS models to model name, different number of parameters
in each case.

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

split_names function

<dl><dt>models</dt>
<dd>vector of model names</dd></dl>

Arguments

split the names of ARIMA and ETS models — split_names

<dl>

<dt>models</dt>
<dd>vector of model names</dd>

</dl>

split_names: split the names of ARIMA and ETS models

Description

Usage

Value

Arguments