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: 1.1.4
Depends: R (≥ 3.2.3)
Imports: stats, urca, forecast (≥ 8.3), tsfeatures, dplyr, magrittr, randomForest, Mcomp, forecTheta, stringr, tibble, purrr, ForeCA, future, furrr, utils, repmis
Suggests: testthat (≥ 2.1.0), covr
Published: 2020-02-21
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>
License: GPL-3
NeedsCompilation: no
Materials: README
In views: TimeSeries
CRAN checks: seer results

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Reference manual: seer.pdf
Package source: seer_1.1.4.tar.gz
Windows binaries: r-devel: seer_1.1.4.zip, r-release: seer_1.1.4.zip, r-oldrel: seer_1.1.4.zip
macOS binaries: r-release: seer_1.1.4.tgz, r-oldrel: seer_1.1.4.tgz

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