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type: software
license: GPL-3.0-only
title: 'seer: Feature-Based Forecast Model Selection'
version: 1.1.8
doi: 10.32614/CRAN.package.seer
abstract: 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>.
authors:
- family-names: Talagala
  given-names: Thiyanga
  email: tstalagala@gmail.com
  orcid: https://orcid.org/0000-0002-0656-9789
- family-names: Hyndman
  given-names: Rob J
  orcid: https://orcid.org/0000-0002-2140-5352
- family-names: Athanasopoulos
  given-names: George
repository: https://robjhyndman.r-universe.dev
repository-code: https://github.com/thiyangt/seer
commit: abd4c2aa172809146df850db2da64310fa4ae98e
url: https://thiyangt.github.io/seer/
contact:
- family-names: Talagala
  given-names: Thiyanga
  email: tstalagala@gmail.com
  orcid: https://orcid.org/0000-0002-0656-9789