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 |