 Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodologyBangzhu Zhu and Yi-Ming Wei Omega, 2013, vol. 41, issue 3, 517-524 Abstract: In general, due to inherently high complexity, carbon prices simultaneously contain linear and nonlinear patterns. Although the traditional autoregressive integrated moving average (ARIMA) model has been one of the most popular linear models in time series forecasting, the ARIMA model cannot capture nonlinear patterns. The least squares support vector machine (LSSVM), a novel neural network technique, has been successfully applied in solving nonlinear regression estimation problems. Therefore, we propose a novel hybrid methodology that exploits the unique strength of the ARIMA and LSSVM models in forecasting carbon prices. Additionally, particle swarm optimization (PSO) is used to find the optimal parameters of LSSVM in order to improve the prediction accuracy. For verification and testing, two main future carbon prices under the EU ETS were used to examine the forecasting ability of the proposed hybrid methodology. The empirical results obtained demonstrate the appeal of the proposed hybrid methodology for carbon price forecasting. Keywords: Least squares support vector machine; ARIMA; Particle swarm optimization; Hybrid models; Time series forecasting; Carbon price (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jomega:v:41:y:2013:i:3:p:517-524 Ordering information: This journal article can be ordered from DOI: 10.1016/j.omega.2012.06.005 Access Statistics for this article Omega is currently edited by B. Lev More articles in Omega from Elsevier |
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