Onlineforecast: An R Package for Adaptive and Recursive Forecasting

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.

Peder Bacher (Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark) , Hjörleifur G. Bergsteinsson (Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark) , Linde Frölke (Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark) , Mikkel L. Sørensen (Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark) , Julian Lemos-Vinasco (Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark) , Jon Liisberg (Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark) , Jan Kloppenborg Møller (Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark) , Henrik Aalborg Nielsen (ENFOR A/S) , Henrik Madsen (Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark)
2023-09-07

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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}
}