cal_sMAPE

training

test

forecast

<p>Calculation of symmetric mean absolute percentage error</p>

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

symmetric Mean Absolute Pecentage Error(sMAPE) — cal_sMAPE

symmetric Mean Absolute Pecentage Error(sMAPE)

cal_sMAPE: symmetric Mean Absolute Pecentage Error(sMAPE)

Description

Usage

Arguments

Value