Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?
Grzegorz Marcjasz (),
Bartosz Uniejewski () and
Rafał Weron ()
No HSC/18/05, HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Technology
A recent electricity price forecasting (EPF) study has shown that the Seasonal Component Artificial Neural Network (SCANN) modeling framework, which consists of decomposing a series of spot prices into a 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 non-linear autoregressive NARX-type neural network is calibrated to the prices themselves. Here, considering two novel extensions of the SCANN concept to probabilistic forecasting, we find that (i) efficiently calibrated 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. Moreover, we confirm an earlier observation that averaging probabilities outperforms averaging quantiles when combining predictive distributions in EPF.
Keywords: Electricity spot price; Probabilistic forecast; Combining forecasts; Long-term seasonal component; NARX neural network; Quantile regression (search for similar items in EconPapers)
JEL-codes: C14 C22 C45 C51 C53 Q47 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene, nep-ets and nep-for
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