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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Veröffentlicht in:	Energies (Basel) Jg. 14; H. 9; S. 2352
Hauptverfasser:	Zhang, Yang, Peng, Yidong, Qu, Xiuli, Shi, Jing, Erdem, Ergin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.05.2021
Schlagworte:	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
Online-Zugang:	Volltext
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