Carbon Price Forecasting Using a Parameters Simultaneous Optimized Least Squares Support Vector Machine with Uniform Design

This chapter augments the least squares support vector machine (LSSVM) approach of forecasting carbon prices by adding the uniform design (UD) feature. Compared with the particle swarm optimization (PSO) feature, uniform design displays optimization efficiency advantages in complex (low/high) carbon price environments.

[1]  Bangzhu Zhu A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network , 2012 .

[2]  Ying-Cheng Lai,et al.  Correlation-dimension and autocorrelation fluctuations in epileptic seizure dynamics. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Julien Chevallier,et al.  On the realized volatility of the ECX CO2 emissions 2008 futures contract: distribution, dynamics and forecasting , 2009 .

[4]  Yi-Ming Wei,et al.  Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology , 2013 .

[5]  Stefan Trück,et al.  Modeling the Price Dynamics of Co2 Emission Allowances , 2009 .

[6]  Weiping Zhang,et al.  Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm , 2013, Knowl. Based Syst..

[7]  S. Byun,et al.  Forecasting carbon futures volatility using GARCH models with energy volatilities , 2013 .

[8]  Kin Keung Lai,et al.  Credit scoring using support vector machines with direct search for parameters selection , 2008, Soft Comput..

[9]  Sergey Paltsev,et al.  An Analysis of the European Emission Trading Scheme , 2005 .

[10]  Julien Chevallier,et al.  Volatility forecasting of carbon prices using factor models , 2010 .

[11]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[12]  Gary Koop,et al.  Forecasting the European carbon market , 2013 .

[13]  L. Cao Practical method for determining the minimum embedding dimension of a scalar time series , 1997 .

[14]  J. Salas,et al.  Nonlinear dynamics, delay times, and embedding windows , 1999 .

[15]  T. Martin McGinnity,et al.  Predicting a Chaotic Time Series using Fuzzy Neural network , 1998, Inf. Sci..

[16]  Yi-Ming Wei,et al.  An overview of current research on EU ETS: Evidence from its operating mechanism and economic effect , 2010 .

[17]  Marc S. Paolella,et al.  An econometric analysis of emission allowance prices , 2008 .

[18]  Andrew M. Fraser,et al.  Information and entropy in strange attractors , 1989, IEEE Trans. Inf. Theory.

[19]  Julien Chevallier,et al.  Nonparametric modeling of carbon prices , 2011 .

[20]  Lixin Tian,et al.  Chaotic characteristic identification for carbon price and an multi-layer perceptron network prediction model , 2015, Expert Syst. Appl..

[21]  Ajalmar R. da Rocha Neto,et al.  Novel approaches using evolutionary computation for sparse least square support vector machines , 2015, Neurocomputing.

[22]  Christian Conrad,et al.  Modeling and Explaining the Dynamics of European Union Allowance Prices at High-Frequency , 2010 .

[23]  Haifeng Ma,et al.  Optimal earth pressure balance control for shield tunneling based on LS-SVM and PSO , 2011 .