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- ISIDORE Id: 10670/1.26c3c8...
- hal: hal-04408320
- doi: 10.1016/j.ijforecast.2023.12.005
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info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ijforecast.2023.12.005
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info:eu-repo/grantAgreement//864337/EU/Next Generation Modelling and Forecasting of Variable Renewable Generation for Large-scale Integration in Energy Systems and Markets/Smart4RES
info:eu-repo/semantics/OpenAccess
Mots-clés
Beta-transform Linear opinion pool Continuous ranked probability score Post-processing Online convex optimization Renewable energy Probabilistic forecasting European project Forecasts combinationSujets proches
Prediction Forecasts FuturologyCiter ce document
Dennis van der Meer et al., « CRPS-based online learning for nonlinear probabilistic forecast combination », HAL SHS (Sciences de l’Homme et de la Société), ID : 10.1016/j.ijforecast.2023.12.005
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Résumé
Forecast combination improves upon the component forecasts. Most often, combination approaches are restricted to the linear setting only. However, theory shows that if the component forecasts are neutrally dispersed—a requirement for probabilistic calibration—linear forecast combination will only increase dispersion and thus lead to miscalibration. Furthermore, the accuracy of the component forecasts may vary over time and the combination weights should vary accordingly, necessitating updates as time progresses. In this paper, we develop an online version of the beta-transformed linear pool, which theoretically can transform the probabilistic forecasts such that they are neutrally dispersed. We show that, in the case of stationary synthetic time series, the performance of the developed method converges to that of the optimal combination in hindsight. Moreover, in the case of nonstationary real-world time series from a wind farm in mid-west France, the developed model outperforms the optimal combination in hindsight.
