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>.

Version: 1.1.7
Depends: R (≥ 3.2.3)
Imports: stats, urca, forecast (≥ 8.3), dplyr, magrittr, randomForest, forecTheta, stringr, tibble, purrr, future, furrr, utils, tsfeatures, MASS
Suggests: testthat (≥ 2.1.0), covr, repmis, knitr, rmarkdown, ggplot2, tidyr, Mcomp, GGally
Published: 2021-12-08
Author: Thiyanga Talagala ORCID iD [aut, cre], Rob J Hyndman ORCID iD [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

Documentation:

Reference manual: seer.pdf

Downloads:

Package source: seer_1.1.7.tar.gz
Windows binaries: r-devel: seer_1.1.6.zip, r-release: seer_1.1.6.zip, r-oldrel: seer_1.1.6.zip
macOS binaries: r-release (arm64): seer_1.1.6.tgz, r-release (x86_64): seer_1.1.6.tgz, r-oldrel: seer_1.1.6.tgz
Old sources: seer archive

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