Handling volatility and nonlinearity in wind speed data: A comparative analysis between ARIMA-GARCH and ARIMA-MLP

Hussin, Nor Hafizah and Yusoff, Fadhilah and Hairol Anuar, Siti Haryanti and Mohd Said, Rahaini and Samsudin, Adam and Noor Azhuan, Nur Azura and Mohd Yussoff, Nurul Hajar (2024) Handling volatility and nonlinearity in wind speed data: A comparative analysis between ARIMA-GARCH and ARIMA-MLP. Journal of Advanced Research in Applied Mechanics, 121 (1). pp. 44-57. ISSN 2289-7895

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Abstract

One of the notable features of wind speed is its volatility and nonlinearity. Thorough assessment on the presence of these features is crucial to obtain a wind speed forecasting model with higher accuracy. In this study, the conventional time series linear model; ARIMA model was applied to assess the internal structure of the wind speed daily data in two stations in Johor; Senai and Mersing. Due to the existence of some nonlinearity features in the residuals part of ARIMA modelling, two nonlinear models were introduced to capture the internal structure of the residual data. Both conventional time series models; GARCH, and machine learning model; MLP was applied to model the residuals of ARIMA model. The out-sample performance in forecasting accuracy was compared between the ARIMA-GARCH model and the ARIMAMLP model. The findings proves that MLP model has outperformed GARCH model in capturing the dynamics in the residual data by providing the lowest error measurements. Thus, the machine learning approaches has proven its superiority against the conventional time series nonlinear model in handling the nonlinearity in the daily wind speed series for wind speed forecasting.

Item Type:	Article
Uncontrolled Keywords:	Daily wind speed, GARCH, MLP, Volatility, Nonlinearity, forecasting
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	05 Feb 2025 10:42
Last Modified:	05 Feb 2025 10:42
URI:	http://eprints.utem.edu.my/id/eprint/28246
Statistic Details:	View Download Statistic

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