Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks
Florian Ziel and
Rafał Weron ()
Energy Economics, 2018, vol. 70, issue C, 396-420
We conduct an extensive empirical study on short-term electricity price forecasting (EPF) to address the long-standing question if the optimal model structure for EPF is univariate or multivariate. We provide evidence that despite a minor edge in predictive performance overall, the multivariate modeling framework does not uniformly outperform the univariate one across all 12 considered datasets, seasons of the year or hours of the day, and at times is outperformed by the latter. This is an indication that combining advanced structures or the corresponding forecasts from both modeling approaches can bring a further improvement in forecasting accuracy. We show that this indeed can be the case, even for a simple averaging scheme involving only two models. Finally, we also analyze variable selection for the best performing high-dimensional lasso-type models, thus provide guidelines to structuring better performing forecasting model designs.
Keywords: Electricity price forecasting; Day-ahead market; Univariate modeling; Multivariate modeling; Forecast combination; Regression; Variable selection; Lasso (search for similar items in EconPapers)
JEL-codes: C14 C22 C51 C53 Q47 (search for similar items in EconPapers)
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Working Paper: Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:70:y:2018:i:c:p:396-420
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