seer: Feature-Based Forecast Model Selection

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>.

Getting started

Package details

AuthorThiyanga Talagala [aut, cre] (<https://orcid.org/0000-0002-0656-9789>), Rob J Hyndman [ths, aut] (<https://orcid.org/0000-0002-2140-5352>), George Athanasopoulos [ths, aut]
MaintainerThiyanga Talagala <tstalagala@gmail.com>
LicenseGPL-3
Version1.1.6
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("seer")

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seer documentation built on June 1, 2021, 9:07 a.m.