Comprehensive evaluation of ARMA–GARCH(-M) approaches for modeling the mean and volatility of wind speed

Li, Heping; Erdem, Ergin; Shi, Jing

doi:10.1016/j.apenergy.2010.09.028

Cited by 187 publications

(85 citation statements)

References 36 publications

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“…In this paper, we consider four models from the vast literature, namely exponential GARCH (EGARCH), threshold GARCH (TGARCH), Glosten-Jagannathan-Runkle GARCH (GJR GARCH) and nonlinear GARCH (NGARCH). We chose these four model for the following reasons: first, these GARCH models have been widely recommended due to their simplicities and demonstrated abilities to forecast volatility (Brownlees et al, 2011); second, Liu et al (2011) applied the above-mentioned GARCH models for wind speed volatility and found that the volatility of wind speed has the nonlinear and asymmetric time-varying properties. Since wind speed is the main driving factor of wind power production, it is reasonable to refer to wind speed modelling.…”

Section: Variants Of Garch Modelsmentioning

confidence: 99%

“…Tastu et al (2014) use an ARCH model to generate the variances in the probabilistic forecasts of wind power production for an offshore wind farm in Denmark. Liu et al (2011) evaluate the effectiveness of ARMA-GARCH approaches for modeling the mean and volatility of wind speed, including different GARCH models such as EGARCH and TGARCH. Lau and McSharry (2010) identify an ARIMA-EGARCH model for aggregated wind power data in Ireland and produce forecasts of the wind power density up to 24 hours ahead.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting volatility of wind power production

Shen

Ritter

2016

Applied Energy

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Abstract:The increasing share of wind energy in the portfolio of energy sources highlights its uncertainties due to changing weather conditions. To account for the uncertainty in predicting wind power production, this article examines the volatility forecasting abilities of different GARCH-type models for wind power production. Moreover, due to characteristic features of the wind power process, such as heteroscedasticity and nonlinearity, we also investigate the use of a Markov regime-switching GARCH (MRS-GARCH) model on forecasting volatility of wind power. The realized volatility, which is derived from lower-scale data, serves as a benchmark for the latent volatility. We find that the MRS-GARCH model significantly outperforms traditional GARCH models in predicting the volatility of wind power, while the exponential GARCH model is superior among traditional GARCH models.

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Section: Variants Of Garch Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting volatility of wind power production

Shen

Ritter

2016

Applied Energy

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show abstract

“…For this reason, different classes of time series models have been considered to express changes in wind speed and wind direction. For wind speed, linear models such as ARMA (e.g., Philippopoulos and Deligiorgi (2009)) and GARCH (e.g., Tol (1997) and Liu et al (2011)) have been applied for analysis. In contrast, wind direction time series frequently tend to show rapid changes, which have different characteristics than those of wind speed.…”

Section: A Statistical Methods For Forecasting Wave-height Changes Fromentioning

confidence: 99%

A Statistical Approach for Wave-Height Forecast Based on Spatiotemporal Variation of Surface Wind

Hokimoto¹

2013

Topics in Oceanography

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“…Both short-and long-memory processes (modeled by ARMA, FARIMA, or other models) are commonly used in practice to describe all kinds of time-varying data, including eolic phenomena [10,11], biomedical signals [12], or financial markets [13,14]. Our goal in this paper is to consider a generic formulation of time-series so that we accommodate diverse memory properties in a unified framework.…”

Section: Introductionmentioning

confidence: 99%

Sequential Monte Carlo for inference of latent ARMA time-series with innovations correlated in time

Urteaga

Bugallo

Djurić

2017

EURASIP J. Adv. Signal Process.

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We consider the problem of sequential inference of latent time-series with innovations correlated in time and observed via nonlinear functions. We accommodate time-varying phenomena with diverse properties by means of a flexible mathematical representation of the data. We characterize statistically such time-series by a Bayesian analysis of their densities. The density that describes the transition of the state from time t to the next time instant t + 1 is used for implementation of novel sequential Monte Carlo (SMC) methods. We present a set of SMC methods for inference of latent ARMA time-series with innovations correlated in time for different assumptions in knowledge of parameters. The methods operate in a unified and consistent manner for data with diverse memory properties. We show the validity of the proposed approach by comprehensive simulations of the challenging stochastic volatility model.

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Comprehensive evaluation of ARMA–GARCH(-M) approaches for modeling the mean and volatility of wind speed

Cited by 187 publications

References 36 publications

Forecasting volatility of wind power production

Forecasting volatility of wind power production

A Statistical Approach for Wave-Height Forecast Based on Spatiotemporal Variation of Surface Wind

Sequential Monte Carlo for inference of latent ARMA time-series with innovations correlated in time

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