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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Vydáno v:	Energies (Basel) Ročník 14; číslo 9; s. 2352
Hlavní autoři:	Zhang, Yang, Peng, Yidong, Qu, Xiuli, Shi, Jing, Erdem, Ergin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.05.2021
Témata:	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
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Abstract	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–Maximization (EM) algorithm is introduced. The applicability of this methodology is comprehensively evaluated for the forecasting of energy related time series including wind speed, wind power generation, and electricity price. Its forecasting performances are evaluated by various criteria, and also compared with those of the conventional AutoRegressive Moving-Average (ARMA) model and the less conventional ARMA-GARCH model. It is found that the proposed mixture GARCH model outperforms the other two models in terms of volatility modeling for all the energy related time series considered. This is proven to be statistically significant because the p-values of likelihood ratio test are less than 0.0001. On the other hand, in terms of estimations of mean wind speed, mean wind power output, and mean electricity price, no significant improvement from the proposed model is obtained. The results indicate that the proposed finite mixture GARCH model is a viable approach for mitigating the associated risk in energy related predictions thanks to the reduced errors on volatility modeling.
AbstractList	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–Maximization (EM) algorithm is introduced. The applicability of this methodology is comprehensively evaluated for the forecasting of energy related time series including wind speed, wind power generation, and electricity price. Its forecasting performances are evaluated by various criteria, and also compared with those of the conventional AutoRegressive Moving-Average (ARMA) model and the less conventional ARMA-GARCH model. It is found that the proposed mixture GARCH model outperforms the other two models in terms of volatility modeling for all the energy related time series considered. This is proven to be statistically significant because the p-values of likelihood ratio test are less than 0.0001. On the other hand, in terms of estimations of mean wind speed, mean wind power output, and mean electricity price, no significant improvement from the proposed model is obtained. The results indicate that the proposed finite mixture GARCH model is a viable approach for mitigating the associated risk in energy related predictions thanks to the reduced errors on volatility modeling.
Author	Peng, Yidong Zhang, Yang Shi, Jing Erdem, Ergin Qu, Xiuli
Author_xml	– sequence: 1 givenname: Yang surname: Zhang fullname: Zhang, Yang – sequence: 2 givenname: Yidong surname: Peng fullname: Peng, Yidong – sequence: 3 givenname: Xiuli surname: Qu fullname: Qu, Xiuli – sequence: 4 givenname: Jing surname: Shi fullname: Shi, Jing – sequence: 5 givenname: Ergin surname: Erdem fullname: Erdem, Ergin
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SubjectTerms	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
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Title	A Finite Mixture GARCH Approach with EM Algorithm for Energy Forecasting Applications
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