Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?
Grzegorz Marcjasz (),
Bartosz Uniejewski () and
Rafał Weron ()
International Journal of Forecasting, 2020, vol. 36, issue 2, 466-479
Recent electricity price forecasting studies have shown that decomposing a series of spot prices into a long-term trend-seasonal and a stochastic component, modeling them independently and then combining their forecasts, can yield more accurate point predictions than an approach in which the same regression or neural network model is calibrated to the prices themselves. Here, considering two novel extensions of this concept to probabilistic forecasting, we find that (i) efficiently calibrated non-linear autoregressive with exogenous variables (NARX) networks can outperform their autoregressive counterparts, even without combining forecasts from many runs, and that (ii) in terms of accuracy it is better to construct probabilistic forecasts directly from point predictions. However, if speed is a critical issue, running quantile regression on combined point forecasts (i.e., committee machines) may be an option worth considering. Finally, we confirm an earlier observation that averaging probabilities outperforms averaging quantiles when combining predictive distributions in electricity price forecasting.
Keywords: Electricity spot price; Probabilistic forecast; Combining forecasts; Long-term seasonal component; NARX neural network; Quantile regression (search for similar items in EconPapers)
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Working Paper: Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts? (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:2:p:466-479
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