• Corpus ID: 237940091

onlineforecast: An R package for adaptive and recursive forecasting

@inproceedings{Bacher2021onlineforecastAR,
  title={onlineforecast: An R package for adaptive and recursive forecasting},
  author={Peder Bacher and Hj{\"o}rleifur G. Bergsteinsson and Linde Frolke and Mikkel Lindstr{\o}m S{\o}rensen and Julian Lemos-Vinasco and Jon Anders Reichert Liisberg and Jan Kloppenborg M{\o}ller and Henrik Aalborg Nielsen and Henrik Madsen},
  year={2021}
}
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 linear regression-based models. Furthermore, dynamical and non-linear effects can be easily… 

Figures from this paper

References

SHOWING 1-10 OF 35 REFERENCES
ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling
TLDR
Real application examples with natural time series datasets are presented to exhibit the features of the ForecastTB package to evaluate forecasting comparison analysis as affected by the characteristics of a dataset.
An introduction to multivariate probabilistic forecast evaluation
TLDR
A review of a selected set of probabilistic forecast evaluation methods, primarily scoring rules, as well as practical sections that explain how these methods can be calculated and estimated.
Automatic Time Series Forecasting: The forecast Package for R
TLDR
Two automatic forecasting algorithms that have been implemented in the forecast package for R, based on innovations state space models that underly exponential smoothing methods, are described.
Online short-term solar power forecasting
TLDR
The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h, where the results indicate that for forecasts up to 2 h ahead the most important input is the available observations ofSolar power, while for longer horizons NWPs are theMost important input.
Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts
TLDR
An existing wind power forecasting system (Zephyr/WPPT) is considered and it is shown how analysis of the forecast error can be used to build a model of the quantiles of the Forecast Error, whereby the model obtained can beused for providing situation-dependent information regarding the uncertainty.
PREDICTION OF WIND POWER USING TIME-VARYING COEFFICIENT-FUNCTIONS
A method for adaptive and recursive estimation in a class of non-linear autore- gressive models with external input is proposed. The model class considered is conditionally parametric ARX-models
Forecasting with Exponential Smoothing: The State Space Approach
I. Introduction: Basic concepts.- Getting started. II. Essentials: Linear innovations state space models.- Non-linear and heteroscedastic innovations state space models.- Estimation of innovations
Probabilistic load forecasting considering temporal correlation: Online models for the prediction of households’ electrical load
TLDR
Two new modelling methods are presented, both suited for producing multivariate probabilistic forecasts, which consider the temporal correlation between forecast horizons, and a quantile–copula ensemble outperforming the RLS-based models in predicting the marginal distributions and capturing theporal correlation.
Out-of-sample tests of forecasting accuracy: an analysis and review
TLDR
The structure of out-of-sample tests is explained, guidelines for implementing these tests are provided, and the adequacy of out of-offer tests in forecasting software is evaluated.
An R Package for Dynamic Linear Models
TLDR
An R package focused on Bayesian analysis of dynamic linear models with flexibility to deal with a variety of constant or time-varying, univariate or multivariate models, and the numerically stable singular value decomposition-based algorithms used for filtering and smoothing is described.
...
1
2
3
4
...