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 <>.

Version: 1.1.8
Depends: R (≥ 3.2.3)
Imports: stats, urca, forecast (≥ 8.3), dplyr, magrittr, randomForest, forecTheta, stringr, tibble, purrr, future, furrr, utils, tsfeatures
Suggests: testthat (≥ 2.1.0), covr, repmis, knitr, rmarkdown, ggplot2, tidyr, Mcomp, GGally
Published: 2022-10-01
DOI: 10.32614/CRAN.package.seer
Author: Thiyanga Talagala ORCID iD [aut, cre], Rob J Hyndman ORCID iD [ths, aut], George Athanasopoulos [ths, aut]
Maintainer: Thiyanga Talagala <tstalagala at>
License: GPL-3
NeedsCompilation: no
Materials: README
In views: TimeSeries
CRAN checks: seer results


Reference manual: seer.pdf


Package source: seer_1.1.8.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): seer_1.1.8.tgz, r-oldrel (arm64): seer_1.1.8.tgz, r-release (x86_64): seer_1.1.8.tgz, r-oldrel (x86_64): seer_1.1.8.tgz
Old sources: seer archive


Please use the canonical form to link to this page.