Computing electricity spot price prediction intervals using quantile regression and forecast averaging

DOI10.1007/s00180-014-0523-0zbMath1342.65056OpenAlexW2066035378WikidataQ29041983 ScholiaQ29041983MaRDI QIDQ740072

Rafał Weron, Jakub Nowotarski

Publication date: 12 August 2016

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-014-0523-0

zbMATH Keywords

quantile regressionprediction intervalelectricity spot priceforecasts combinationquantile regression averaging

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Computational methods for problems pertaining to statistics (62-08)

Related Items (6)

Easy Uncertainty Quantification (EasyUQ): Generating Predictive Distributions from Single-Valued Model Output ⋮ Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data ⋮ Unnamed Item ⋮ Modeling seasonality and serial dependence of electricity price curves with warping functional autoregressive dynamics ⋮ Editorial to the special issue on applicable semiparametrics of computational statistics ⋮ Estimation combining unbiased and possibly biased estimators

Cites Work

• Review of guidelines for the use of combined forecasts
• Electricity price forecasting through transfer function models
• Regression Quantiles
• Nonparametric maximum likelihood estimators for ar and ma time series
• Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models

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