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 resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4