Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models

By Adam Misiorek, Stefan Trueck, Rafal Weron. 2006

249 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/84aacbdfeab034be8c464450c3361001e113a262

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Citations in the corpus

Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, background): “The second on the list is Rafał Weron (Wrocław University of Technology, Poland) with 11 Scopus-indexed articles, including the most comprehensive EPF review to date – Electricity price forecasting: A review of the state-of-the-art with a look into the future [1] (103 citations since 2014); Weron ha”
Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling (2013, background): “On the other hand, Misiorek et al. (2006) find that, despite the heteroscedastic nature of the residuals in the autoregressive models, the addition of a GARCH component i the specification does not improve the accuracy of point forecasts though it slightly improves interval forecasts.”
Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, methodology): “To simplify this task even more, some studies perform the forecasts separately across the hours, thus eliminating the need for explicitmodeling of the daily price profile, but leading to 24 (or 48) sets of parameters (see e.g. Karakatsani & Bunn, 2008, 2010; Misiorek et al., 2006; Raviv, Bouwman, & ”
Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging (2016, methodology): “Note that models ARX and AR are very similar to models (with the same names) considered by Kristiansen (2012), Misiorek et al. (2006), Nowotarski and Weron (2014a) and Weron and Misiorek (2008) among others.”
MULTI-FACTOR JUMP-DIFFUSION MODELS OF ELECTRICITY PRICES (2008, background): “We mention that there also exist a variety of econometric time series models (see e.g. [7, 12, 13, 14] and references therein).”
Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape (2011, background): “From 2006, only few authors have complemented the stochastic point forecasts with confidence intervals (e.g., Misiorek and Weron, 2006; Misiorek et al., 2006; Nogales and Conejo, 2006; Weron and Misiorek, 2008).”
Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models (2008, methodology): “…(or ARMAX) models (Conejo et al., 2005), vector autoregressions with exogenous effects (Panagiotelis and Smith, 2008), threshold AR and ARX models (Misiorek et al., 2006), regime-switching regressions with fundamental variables (Karakatsani and Bunn, 2008) and mean-reverting jump diffusions…”
An empirical comparison of alternate schemes for combining electricity spot price forecasts (2013, unknown): “Thus, when dealing with the modeling and forecasting of intraday prices it is important to recall that prices for all spot contracts of the next day are determined at the same time using the same available information (Conejo et al., 2005; Huisman et al., 2007; Misiorek et al., 2006; Peña, 2012).”
On the importance of the long-term seasonal component in day-ahead electricity price forecasting (2016, methodology): “We have shown that, all considered autoregressive models – both SCAR-type and the ARXtype benchmarks – beat the naı̈ve benchmark of Nogales et al. (2002) by a large margin, which indicates that they all are highly efficient forecasting tools. Furthermore, we have provided evidence that a SCAR-type m”