fforms_combinationforecast

a list output from fforms_ensemble function

fforms.ensemble

tslist

whethe the time series is from mcom or other

database

if holdout=TRUE take a holdout sample from your data to caldulate forecast accuracy measure, if FALSE all of the data will be used for forecasting. Default is TRUE

holdout

If TRUE, multiple cores (or multiple sessions) will be used. This only speeds things up
when there are a large number of time series.

parallel

Compute combination forecast based on the vote matrix probabilities

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>.

fforms_combinationforecast: Combination forecast based on fforms

Description

Usage

Arguments

Value