Convenient functions for ensemble forecasts in R combining approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(), thetaf(), nnetar(), stlm(), tbats(), snaive() and arfima() can be combined with equal weights, weights based on in-sample errors (introduced by Bates & Granger (1969) <doi:10.1057/jors.1969.103>), or cross-validated weights. Cross validation for time series data with user-supplied models and forecasting functions is also supported to evaluate model accuracy.
Version: 5.1.20 Depends: R (≥ 4.0.4), forecast (≥ 8.16), thief Imports: doParallel (≥ 1.0.16), foreach (≥ 1.5.1), ggplot2 (≥ 3.3.6), purrr (≥ 0.3.5), zoo (≥ 1.8) Suggests: GMDH, knitr, rmarkdown, roxygen2, testthat Published: 2025-07-06 DOI: 10.32614/CRAN.package.forecastHybrid Author: David Shaub [aut, cre], Peter Ellis [aut] Maintainer: David Shaub <davidshaub at alumni.harvard.edu> BugReports: https://github.com/ellisp/forecastHybrid/issues License: GPL-3 URL: https://gitlab.com/dashaub/forecastHybrid, https://github.com/ellisp/forecastHybrid NeedsCompilation: no Materials: NEWS In views: TimeSeries CRAN checks: forecastHybrid results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=forecastHybrid to link to this page.
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