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:	1.1.5
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:	2020-06-08
Author:	Thiyanga Talagala [aut, cre], Rob J Hyndman [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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