forecastHybrid: Convenient Functions for Ensemble Time Series Forecasts
Convenient functions for ensemble forecasts in R combining
approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(),
thetam(), nnetar(), stlm(), and tbats() 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: |
3.0.14 |
| Depends: |
R (≥ 3.1.1), forecast (≥ 8.1) |
| Imports: |
doParallel (≥ 1.0.10), foreach (≥ 1.4.3), ggplot2 (≥
2.2.0), zoo (≥ 1.7) |
| Suggests: |
GMDH, knitr, rmarkdown, roxygen2, testthat |
| Published: |
2018-07-22 |
| Author: |
David Shaub [aut, cre],
Peter Ellis [aut] |
| Maintainer: |
David Shaub <davidshaub at gmx.com> |
| BugReports: |
https://github.com/ellisp/forecastHybrid/issues |
| License: |
GPL-3 |
| URL: |
https://gitlab.com/dashaub/forecastHybrid |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| In views: |
TimeSeries |
| CRAN checks: |
forecastHybrid results |
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Reverse dependencies:
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