Feature-Based Forecast Model Selection R package seer version 1.1.8 CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries) 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>. Software 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 Thiyanga Talagala <tstalagala@gmail.com> Comprehensive R Archive Network (CRAN) Thiyanga Talagala [aut, cre] (ORCID: <https://orcid.org/0000-0002-0656-9789>), Rob J Hyndman [ths, aut] (ORCID: <https://orcid.org/0000-0002-2140-5352>), George Athanasopoulos [ths, aut] GPL-3 2022-10-01 application/tgz https://CRAN.R-project.org/package=seer doi:10.32614/CRAN.package.seer