# onlineforecast: An R package for adaptive and recursive forecasting

arXiv: https://arxiv.org/abs/2109.12915 · HTML: https://mlsanity.com/paper/2109.12915/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 37 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Builds on (most cited first, 1 of 1)

- [KFAS: Exponential Family State Space Models in R](https://mlsanity.com/paper/1612.01907/) (2016)

## Abstract (arXiv)

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 neural network 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.