cal_sMAPE

Calculation of symmetric mean absolute percentage error

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

cal_sMAPE function

<dl><dt>training</dt>
<dd>training peiod of the time series</dd>
<dt>test</dt>
<dd>test period of the time series</dd>
<dt>forecast</dt>
<dd>forecast values of the series</dd></dl>

Arguments

Author

symmetric Mean Absolute Pecentage Error(sMAPE) — cal_sMAPE

<dl>

<dt>training</dt>
<dd>training peiod of the time series</dd>


<dt>test</dt>
<dd>test period of the time series</dd>


<dt>forecast</dt>
<dd>forecast values of the series</dd>

</dl>

cal_sMAPE: symmetric Mean Absolute Pecentage Error(sMAPE)

Description

Usage

Value

Arguments

Author