Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting

Grzegorz Marcjasz, Tomasz Serafin and Rafał Weron

Energies, 2018, vol. 11, issue 9, 1-20

Abstract: We conduct an extensive empirical study on the selection of calibration windows for day-ahead electricity price forecasting, which involves six year-long datasets from three major power markets and four autoregressive expert models fitted either to raw or transformed prices. Since the variability of prediction errors across windows of different lengths and across datasets can be substantial, selecting ex-ante one window is risky. Instead, we argue that averaging forecasts across different calibration windows is a robust alternative and introduce a new, well-performing weighting scheme for averaging these forecasts.

Keywords: electricity price forecasting; forecast averaging; calibration window; autoregression; variance stabilizing transformation; conditional predictive ability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (41)

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Working Paper: Selection of calibration windows for day-ahead electricity price forecasting (2018)
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