LSSVR ensemble learning with uncertain parameters for crude oil price forecasting

Display Omitted An LSSVR ensemble learning paradigm with uncertain parameters is proposed.The user-defined parameters in LSSVR are treated as uncertain variables.Uncertain parameters are first used to formulate diverse individual members.Uncertainties are then offset via ensemble weighed averaging.Empirical study of crude oil price prediction verifies effectiveness of the model. Least squares support vector regression (LSSVR) is an effective and competitive approach for crude oil price prediction, but its performance suffers from parameter sensitivity and long tuning time. This paper considers the user-defined parameters as uncertain (or random) factors to construct an LSSVR ensemble learning paradigm, by taking four major steps. First, probability distributions of the user-defined parameters in LSSVR are designed using grid method for low upper bound estimation (LUBE). Second, random sets of parameters are generated according to the designed probability distributions to formulate diverse individual LSSVR members. Third, each individual member is applied to individual prediction. Finally, all individual results are combined to the final output via ensemble weighted averaging, with probabilities measuring the corresponding weights. The computational experiment using the crude oil spot price of West Texas Intermediate (WTI) verifies the effectiveness of the proposed LSSVR ensemble learning paradigm with uncertain parameters compared with some existing LSSVR variants (using other popular parameters selection algorithms), in terms of prediction accuracy and time-saving.

[1]  Abbas Khosravi,et al.  Uncertainty handling using neural network-based prediction intervals for electrical load forecasting , 2014 .

[2]  Kostas Andriosopoulos,et al.  Modelling energy spot prices: Empirical evidence from NYMEX , 2012 .

[3]  Almoataz Y. Abdelaziz,et al.  Optimal placement and sizing of voltage controlled distributed generators in unbalanced distribution networks using supervised firefly algorithm , 2016 .

[4]  Kin Keung Lai,et al.  Hybrid approaches based on LSSVR model for container throughput forecasting: A comparative study , 2013, Appl. Soft Comput..

[5]  Alessandro Gallo,et al.  A simple method to achieve a uniform flux distribution in a multi-faceted point focus concentrator , 2016 .

[6]  Ling Tang,et al.  A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting , 2015, Ann. Oper. Res..

[7]  Terufumi Yokota,et al.  Random Ising antiferromagnet on Bethe-like lattices with triangular loops , 2016 .

[8]  Qian Liu,et al.  Weighted least squares support vector machine local region method for nonlinear time series prediction , 2010, Appl. Soft Comput..

[9]  A. Lanza,et al.  Modeling and forecasting cointegrated relationships among heavy oil and product prices , 2005 .

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

[11]  Wei-Chiang Hong,et al.  SVR with hybrid chaotic genetic algorithms for tourism demand forecasting , 2011, Appl. Soft Comput..

[12]  A. Selakov,et al.  Hybrid PSO-SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank , 2014, Appl. Soft Comput..

[13]  Li Shurong,et al.  Crude Oil Price Prediction Based on a Dynamic Correcting Support Vector Regression Machine , 2013 .

[14]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

[15]  Min Liu.  Probabilistic prediction of green roof energy performance under parameter uncertainty , 2014 .

[16]  Piotr Nowak,et al.  The MLE of the mean of the exponential distribution based on grouped data is stochastically increasing , 2016 .

[17]  Ling Tang,et al.  A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting , 2016, Neural Computing and Applications.

[18]  Ling Tang,et al.  A compressed sensing based AI learning paradigm for crude oil price forecasting , 2014 .

[19]  Ekin Tokat,et al.  Forecasting oil price movements with crack spread futures , 2009 .

[20]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[21]  Bahman Mehdizadeh,et al.  Forecasting the differences between various commercial oil prices in the Persian Gulf region by neur , 2011 .

[22]  Wiktor Ejsmont,et al.  A Characterization of the Normal Distribution by the Independence of a Pair of Random Vectors , 2016, 1601.00078.