Interval decomposition ensemble approach for crude oil price forecasting

Crude oil is one of the most important energy sources in the world, and it is very important for policymakers, enterprises and investors to forecast the price of crude oil accurately. This paper proposes an interval decomposition ensemble (IDE) learning approach to forecast interval-valued crude oil price by integrating bivariate empirical mode decomposition (BEMD), interval MLP (MLPI) and interval exponential smoothing method (HoltI). Firstly, the original interval-valued crude oil price is transformed into a complex-valued signal. Secondly, BEMD is used to decompose the constructed complex-valued signal into a finite number of complex-valued intrinsic mode functions (IMFs) components and one complex-valued residual component. Thirdly, MLPI is used to simultaneously forecast the lower and the upper bounds of each IMF (non-linear patterns), and HoltI is used for modeling the residual component (linear pattern). Finally, the forecasting results of the lower and upper bounds of all the components are combined to generate the aggregated interval-valued output by employing another MLPI as the ensemble tool. The empirical results show that our proposed IDE learning approach with different forecasting horizons and different data frequencies significantly outperforms some other benchmark models by means of forecasting accuracy and hypothesis tests.

[1]  Zebin Yang,et al.  Online big data-driven oil consumption forecasting with Google trends , 2019, International Journal of Forecasting.

[2]  Shouyang Wang,et al.  Threshold autoregressive models for interval-valued time series data , 2018, Journal of Econometrics.

[3]  Zhongyi Hu,et al.  Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting , 2014, Knowl. Based Syst..

[4]  Jinliang Zhang,et al.  A novel hybrid method for crude oil price forecasting , 2015 .

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

[6]  K. Lai,et al.  Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm , 2008 .

[7]  Shaolong Sun,et al.  A new dynamic integrated approach for wind speed forecasting , 2017 .

[8]  Ling Tang,et al.  A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting , 2016, Eng. Appl. Artif. Intell..

[9]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[10]  Raymond Chiong,et al.  Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection , 2015 .

[11]  Yu Wei,et al.  Forecasting crude oil market volatility: Further evidence using GARCH-class models , 2010 .

[12]  Understanding Crude Oil Prices , 2008 .

[13]  Zhongyi Hu,et al.  Interval Forecasting of Electricity Demand: A Novel Bivariate EMD-based Support Vector Regression Modeling Framework , 2014, ArXiv.

[14]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[15]  Yukun Bao,et al.  Interval-valued time series forecasting using a novel hybrid HoltI and MSVR model , 2017 .

[16]  Kin Keung Lai,et al.  Global economic activity and crude oil prices: A cointegration analysis , 2010 .

[17]  L. Kilian.  Not All Oil Price Shocks are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market , 2006 .

[18]  Danilo P. Mandic,et al.  Multiscale Image Fusion Using Complex Extensions of EMD , 2009, IEEE Transactions on Signal Processing.

[19]  Andre Luis Santiago Maia,et al.  Holt’s exponential smoothing and neural network models for forecasting interval-valued time series , 2011 .

[20]  Bruce Abramson,et al.  Probabilistic forecasts from probabilistic models: A case study in the oil market , 1995 .

[21]  K. Lai,et al.  A new approach for crude oil price analysis based on Empirical Mode Decomposition , 2008 .

[22]  C. Aloui,et al.  Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling , 2012 .

[23]  Lu Zhang,et al.  A combination method for interval forecasting of agricultural commodity futures prices , 2015, Knowl. Based Syst..

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

[25]  Francisco de A. T. de Carvalho,et al.  Constrained linear regression models for symbolic interval-valued variables , 2010, Comput. Stat. Data Anal..

[26]  Chin Wen Cheong,et al.  Modeling and forecasting crude oil markets using ARCH-type models , 2009 .

[27]  Ahmed A. El-Masry,et al.  Oil price forecasting using gene expression programming and artificial neural networks , 2016 .

[28]  Shouyang Wang,et al.  A Clustering-Based Nonlinear Ensemble Approach for Exchange Rates Forecasting , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[29]  Sunghee Choi,et al.  Prediction of movement direction in crude oil prices based on semi-supervised learning , 2013, Decis. Support Syst..

[30]  Raymond Chiong,et al.  Forecasting interval time series using a fully complex-valued RBF neural network with DPSO and PSO algorithms , 2015, Inf. Sci..

[31]  Gabriel Rilling,et al.  Bivariate Empirical Mode Decomposition , 2007, IEEE Signal Processing Letters.

[32]  Michael Ye,et al.  A monthly crude oil spot price forecasting model using relative inventories , 2005 .

[33]  Yongmiao Hong,et al.  Analysis of crisis impact on crude oil prices: a new approach with interval time series modelling , 2016 .

[34]  Jianping Li,et al.  A deep learning ensemble approach for crude oil price forecasting , 2017 .

[35]  Francisco de A. T. de Carvalho,et al.  Forecasting models for interval-valued time series , 2008, Neurocomputing.