Forecast Combination Methods

Provides geometric- and regression-based forecast combination methods under a unified user interface for the packages 'ForecastCombinations' and 'GeomComb'. Additionally, updated tools and convenience functions for data pre-processing are available in order to deal with common problems in forecast combination (missingness, collinearity). For method details see Hsiao C, Wan SK (2014). , Hansen BE (2007). , Elliott G, Gargano A, Timmermann A (2013). , and Clemen RT (1989). .


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The R package ForecastComb presents functions to pool individual model forecasts using geometric- and regression-based forecast combination methods. ForecastComb combines the functionality of the packages ForecastCombinations and GeomComb under a unified user interface and convenience functions.

The forecast combination methods allow for 3 different input types:

  1. Only training set

  2. Training set + future forecasts

  3. Full training + test set

Accuracy measures are provided accordingly, summary and plot functions have been created for the S3 classes. The function auto.combine() is an automated selection of the best combination method based on criterion optimisation in the training set.

Installation

Get started by installing the R software for statistical computing.

You can install the stable version on CRAN:

install.packages('ForecastComb', dependencies = TRUE)

You can also install the development version from Github

# install.packages("devtools")
devtools::install_github("ceweiss/ForecastComb")

License

This package is free and open source software, licensed under GPL (>= 2).

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Reference manual

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install.packages("ForecastComb")

1.3.1 by Christoph E. Weiss, a year ago


https://github.com/ceweiss/ForecastComb


Report a bug at https://github.com/ceweiss/ForecastComb/issues


Browse source code at https://github.com/cran/ForecastComb


Authors: Christoph E. Weiss , Gernot R. Roetzer , Eran Raviv


Documentation:   PDF Manual  


Task views: Time Series Analysis


GPL (>= 2) license


Imports forecast, ggplot2, Matrix, mtsdi, psych, quadprog, quantreg

Suggests testthat


See at CRAN