Onlineforecast: An R Package for Adaptive and Recursive Forecasting

Abstract:

Systems that rely on forecasts to make decisions, e.g. control or energy trading systems, require frequent updates of the forecasts. Usually, the forecasts are updated whenever new observations become available, hence in an online setting. We present the R package onlineforecast that provides a generalized setup of data and models for online forecasting. It has functionality for time-adaptive fitting of dynamical and non-linear models. The setup is tailored to enable the effective use of forecasts as model inputs, e.g. numerical weather forecast. Users can create new models for their particular applications and run models in an operational setting. The package also allows users to easily replace parts of the setup, e.g. using new methods for estimation. The package comes with comprehensive vignettes and examples of online forecasting applications in energy systems, but can easily be applied for online forecasting in all fields.

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Published

Sept. 7, 2023

Received

May 20, 2022

DOI

10.32614/RJ-2023-031

Volume

Pages

15/1

173 - 194


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    Citation

    For attribution, please cite this work as

    Bacher, et al., "Onlineforecast: An R Package for Adaptive and Recursive Forecasting", The R Journal, 2023

    BibTeX citation

    @article{RJ-2023-031,
      author = {Bacher, Peder and Bergsteinsson, Hjörleifur G. and Frölke, Linde and Sørensen, Mikkel L. and Lemos-Vinasco, Julian and Liisberg, Jon and Møller, Jan Kloppenborg and Nielsen, Henrik Aalborg and Madsen, Henrik},
      title = {Onlineforecast: An R Package for Adaptive and Recursive Forecasting},
      journal = {The R Journal},
      year = {2023},
      note = {https://doi.org/10.32614/RJ-2023-031},
      doi = {10.32614/RJ-2023-031},
      volume = {15},
      issue = {1},
      issn = {2073-4859},
      pages = {173-194}
    }