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>.
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 [aut, cre], Rob J Hyndman [ths, aut], George Athanasopoulos [ths, aut] |
Maintainer: | Thiyanga Talagala <tstalagala at gmail.com> |
BugReports: | https://github.com/thiyangt/seer/issues |
License: | GPL-3 |
URL: | https://thiyangt.github.io/seer/ |
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: seer_1.1.8.zip, r-release: seer_1.1.8.zip, r-oldrel: seer_1.1.8.zip |
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 |
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