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 |