Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling

By Joanna Janczura, Stefan Trück, Rafal Weron, Rodney Wolff. 2013

215 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/6201f7c1ff14cbeddc8268be9515a846a4af35c4

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In Electrical and Electronic Engineering

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

An empirical comparison of alternate schemes for combining electricity spot price forecasts (2013, unknown): “…unlike in derivatives pricing and risk management applications where daily average spot prices are typically used (see e.g. Bierbrauer et al., 2007; Janczura et al., 2013), in day-ahead forecasting of hourly electricity prices the long-term trend-seasonal component is usually not taken into…”
Electricity markets around the world (2018, methodology): “Following Janczura et al. (2013b), we also decided to estimate the annual, weekly and intra-daily seasonal patterns based on outlier-filtered data.”
On the importance of the long-term seasonal component in day-ahead electricity price forecasting (2016, methodology): “Motivated by a series of recent articles on modeling and forecasting the LTSC of electricity spot prices (see Janczura et al., 2013; Lisi and Nan, 2014; Nowotarski et al., 2013; Weron and Zator, 2015, among others), we consider two well-performing model classes – the Hodrick-Prescott (HP) and…”
Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, methodology): “…technique used in this figure – the recursive filter on prices (RFP) – classifies as spikes all prices that exceed the mean price level by three standard deviations, with the outlying observations being removed one by one in a ‘recursive filter’ fashion (for details, see Janczura et al., 2013).”
Modeling and forecasting multivariate electricity price spikes (2016, background): “Other definitions of spikes based on statistical arguments have been proposed, by, e.g., Chan et al. (2008), Korniichuk (2012), Janczura et al. (2013) and Eichler and Türk (2013).”
Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, background): “They can be treated as any other price (as in most of the reviewed above papers), treated as outliers [60] and the input prices pre-filtered to minimize or eliminate spikes [22, 24, 61–63] or they can be treated as the main object of study, as in spike occurrence and threshold forecasting.”
Variance Stabilizing Transformations for Electricity Spot Price Forecasting (2018, methodology): “Lower variation and/or less spiky behavior of the input data usually allows the forecasting model to yield more accurate predictions [20].”
Forecasting: theory and practice (2020, methodology): “Janczura et al. (2013) focus on the detection and treatment of outliers in electricity price to improve the general forecasting performance of time series models based on accurate estimation of seasonal and stochastic components, both using original and filtered data.”