A Finite Mixture GARCH Approach with EM Algorithm for Energy Forecasting Applications

Enhancing forecasting performance in terms of both the expected mean value and variance has been a critical challenging issue for energy industry. In this paper, the novel methodology of finite mixture Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) approach with Expectation–Maximi...

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Vydané v:	Energies (Basel) Ročník 14; číslo 9; s. 2352
Hlavní autori:	Zhang, Yang, Peng, Yidong, Qu, Xiuli, Shi, Jing, Erdem, Ergin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.05.2021
Predmet:	Algorithms Alternative energy sources COVID-19 Electricity EM algorithm finite mixture Forecasting GARCH Literature reviews Neural networks Parameter estimation Performance evaluation Prices Renewable resources Stochastic models Time series Volatility wind energy Wind farms Wind power wind speed
ISSN:	1996-1073, 1996-1073
On-line prístup:	Získať plný text
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