onlineforecast: An R package for adaptive and recursive forecasting
This package addresses the need for flexible and efficient online forecasting tools, particularly in energy systems, but it is incremental as it builds on existing forecasting methods within a new software framework.
The paper introduces the R package onlineforecast, which provides a generalized setup for adaptive and recursive forecasting in online settings, enabling frequent updates for decision-making systems like energy trading, with functionality for time-adaptive fitting and support for forecast inputs such as weather predictions.
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.