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September 2019 Modeling seasonality and serial dependence of electricity price curves with warping functional autoregressive dynamics
Ying Chen, J. S. Marron, Jiejie Zhang
Ann. Appl. Stat. 13(3): 1590-1616 (September 2019). DOI: 10.1214/18-AOAS1234

Abstract

Electricity prices are high dimensional, serially dependent and have seasonal variations. We propose a Warping Functional AutoRegressive (WFAR) model that simultaneously accounts for the cross time-dependence and seasonal variations of the large dimensional data. In particular, electricity price curves are obtained by smoothing over the $24$ discrete hourly prices on each day. In the functional domain, seasonal phase variations are separated from level amplitude changes in a warping process with the Fisher–Rao distance metric, and the aligned (season-adjusted) electricity price curves are modeled in the functional autoregression framework. In a real application, the WFAR model provides superior out-of-sample forecast accuracy in both a normal functioning market, Nord Pool, and an extreme situation, the California market. The forecast performance as well as the relative accuracy improvement are stable for different markets and different time periods.

Citation

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Ying Chen. J. S. Marron. Jiejie Zhang. "Modeling seasonality and serial dependence of electricity price curves with warping functional autoregressive dynamics." Ann. Appl. Stat. 13 (3) 1590 - 1616, September 2019. https://doi.org/10.1214/18-AOAS1234

Information

Received: 1 February 2018; Revised: 1 November 2018; Published: September 2019
First available in Project Euclid: 17 October 2019

zbMATH: 07145969
MathSciNet: MR4019151
Digital Object Identifier: 10.1214/18-AOAS1234

Rights: Copyright © 2019 Institute of Mathematical Statistics

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Vol.13 • No. 3 • September 2019
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