### CRUDE OIL PRICE FORECASTING BY CEEMDAN BASED HYBRID MODEL OF ARIMA AND KALMAN FILTER

#### Abstract

This paper used complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) based hybrid model for the forecasting of world crude oil prices. For this purpose, the crude oil prices original time series are decomposed into sub small finite series called intrinsic mode functions (IMFs). Then ARIMA model was applied to each extracted IMF to estimate the parameters. Next, using these estimated parameters of each ARIMA model, the Kalman Filter was run for each IMF, so that these extracted IMFs can be predicted more accurately. Finally, all IMFs are combined to get the result. For testing and verification of the proposed method, two crude oil prices were used as a sample i.e. Brent and WTI (West Texas Intermediate) crude oil monthly prices series. The D-statistic values of the proposed model were 93.33% for Brent and 89.29% for WTI which reveals the importance of the CEEMDAN based hybrid model.

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Watkins, G. and Plourde, A. 1994. How Volatile Are Crude Oil Prices. OPEC Review. 18(4): 220-245.

Alvarez-Ramirez, J., et al. 2003. Symmetry/Anti-Symmetry Phase Transitions in Crude Oil Markets. Physica A: Statistical Mechanics and its Applications. 322: 583-596.

Verleger, P. 1994. Adjusting to Volatile Energy PricesInstitute for International Economics. Washington.

Hagen, R. 1994. How is the International Price of a Particular Crude Determined? OPEC Review. 18(1): 127-135.

Stevens, P. 1995. The Determination of Oil Prices 1945–1995: A Diagrammatic Interpretation. Energy Policy. 23(10): 861-870.

Abosedra, S. and Baghestani, H. 2004. On the Predictive Accuracy Of Crude Oil Futures Prices. Energy Policy. 32(12): 1389-1393.

Wang, S. Y., L. A. Yu and K. K. Lai. 2005. Crude Oil Price Forecasting with TEI@ I Methodology. Journal of Systematic Science. Complexity. 18(2): 145-166.

Huntington, H. G. 1994. Oil Price Forecasting in the 1980s: What Went Wrong? The Energy Journal. 1-22.

Abramson, B. and Finizza, A. 1995. Probabilistic Forecasts from Probabilistic Models: A Case Study in the Oil Market. International Journal of Forecasting. 11(1): 63-72.

Barone-Adesi, G., Bourgoin, F., and Giannopoulos, K. 1998. Don't Look Back. Risk. 11:100-104.

Morana, C. 2001. A Semiparametric Approach to Short-Term Oil Price Forecasting. Energy Economics. 23(3): 325-338.

Gülen, S. G. 1998. Efficiency in the Crude Oil Futures Market. Journal of Energy Finance & Development. 3(1): 13-21.

Mirmirani, S. and Li, H. C. 2004. A Comparison of VAR and Neural Networks with Genetic Algorithm in Forecasting Price of Oil. Advances in Econometrics. 19: 203-223.

Lanza, A., Manera, M., and Giovannini, M. 2005. Modeling and Forecasting Cointegrated Relationships Among Heavy Oil and Product Prices. Energy Economics. 27(6): 831-848.

Ye, M., Zyren, J., and Shore, J. 2002. Forecasting Crude Oil Spot Price using OECD Petroleum Inventory Levels. International Advances in Economic Research. 8(4): 324-333.

Ye, M., Zyren, J., and Shore, J. 2005. A Monthly Crude Oil Spot Price Forecasting Model Using Relative Inventories. International Journal of Forecasting. 21(3): 491-501.

Ye, M., Zyren, J., and Shore, J. 2006. Forecasting Short-run Crude Oil Price Using High-and Low-inventory Variables. Energy Policy. 34(17): 2736-2743.

Dées, S., et al. 2008. Assessing the Factors Behind Oil Price Changes. European Central Bank Working Paper. 4-36.

Liu, L. M. 1991. Dynamic Relationship Analysis of US Gasoline and Crude Oil Prices. Journal of Forecasting. 10(5): 521-547.

Chinn, M. D., LeBlanc, M., and Coibion, O. 2005. The Predictive Content of Energy Futures: An Update on Petroleum, Natural Gas, Heating Oil and Gasoline. National Bureau of Economic Research.

Agnolucci, P. 2009. Volatility in Crude Oil Futures: A Comparison of the Predictive Ability of GARCH and Implied Volatility Models. Energy Economics. 31(2): 316-321.

Ahmad, M. 2012. Modelling and Forecasting Oman Crude Oil Prices Using Box–Jenkins Techniques. International Journal of Trade and Global Markets. 5(1): 24-30.

Sadorsky, P. 2006. Modeling and Forecasting Petroleum Futures Volatility. Energy Economics. 28(4): 467-488.

Hou, A. and Suardi, S. 2012. A Nonparametric GARCH Model of Crude Oil Price Return Volatility. Energy Economics. 34(2): 618-626.

Ahmed, R. A. and Shabri, A. B. 2013. Fitting GARCH Models to Crude Oil Spot Price Data. Life Science Journal. 10(4).

Aamir, M. and Shabri, A. B. 2015. Modelling and Forecasting Monthly Crude Oil Prices of Pakistan: A Comparative Study of ARIMA, GARCH and ARIMA-GARCH Models. Science International. 27(3): 2365-2371.

Diebold, F. X. and Mariano, R. S. 2012. Comparing Predictive Accuracy. Journal Of Business & Economic Statistics. 20(1): 134-144.

Hyndman, R., et al. 2008. Forecasting with Exponential Smoothing: The State Space Approach. Springer Science & Business Media.

Harvey, A. C. 1984. A Unified View of Statistical Forecasting Procedures. Journal of Forecasting. 3(3): 245-275.

Kalman, R. E. 1960. A New Approach to Linear Filtering and Prediction Problems. Journal of Fluids Engineering. 82(1): 35-45.

Morrison, G. W. and Pike, D. H. 1977. Kalman Filtering Applied to Statistical Forecasting. Management Science. 23(7): 768-774.

Rosenberg, B. 1973. Random Coefficients Models: The Analysis of a Cross Section of Time Series by Stochastically Convergent Parameter Regression. Annals of Economic and Social Measurement. 2(4): 399-428.

Engle, R. F. 1979. Estimating Structural Models of Seasonality. Seasonal Analysis of Economic Time Series. NBER. 281-308.

Harvey, A. C. and Phillips, G. D. 1979. Maximum Likelihood Estimation of Regression Models with Autoregressive-Moving Average Disturbances. Biometrika. 66(1): 49-58.

Ravichandran, S. and Prajneshu, J. 2001. State Space Modeling Versus ARIMA Time-Series Modeling. Journal of Indian Society of Agricultural Statistics. 54(1): 43-51.

Nikolaisen Sävås, F. 2013. Forecast Comparison of Models Based on SARIMA and the Kalman Filter for Inflation. Master Thesis, Uppsala Universitet.

Aamir, M. and Shabri, A. 2016. Modelling and Forecasting Monthly Crude Oil Price of Pakistan: A Comparative Study of ARIMA, GARCH and ARIMA Kalman Model. Advances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences (SKSM23). AIP Publishing.

Yu, L., Wang, S., and Lai, K. K. 2008. Forecasting Crude Oil Price with an EMD-based Neural Network Ensemble Learning Paradigm. Energy Economics. 30(5): 2623-2635.

Tang, B., Dong, S., and Song, T. 2012. Method for Eliminating Mode Mixing of Empirical Mode Decomposition Based on the Revised Blind Source Separation. Signal Processing. 92(1): 248-258.

An, X., et al. 2012. Short-term Prediction of Wind Power Using EMD and Chaotic Theory. Communications in Nonlinear Science and Numerical Simulation. 17(2): 1036-1042.

Guo, Z., et al. 2012. Multi-step Forecasting for Wind Speed Using a Modified EMD-based Artificial Neural Network Model. Renewable Energy. 37(1): 241-249.

Huang, N. E. and Wu, Z. 2008. A Review on Hilbert‐Huang Transform: Method and Its Applications to Geophysical Studies. Reviews of Geophysics. 46(2).

Schlotthauer, G., Torres, M. E., and Rufiner, H. L. 2009. A New Algorithm for Instantaneous F 0 Speech Extraction Based on Ensemble Empirical Mode Decomposition. Signal Processing Conference, 2009 17th European. IEEE.

Wu, Z. and Huang, N. E. 2009. Ensemble Empirical Mode Decomposition: A Noise-assisted Data Analysis Method. Advances in Adaptive Data Analysis. 1(01): 1-41.

Wu, Z., et al. 2011. On the Time-varying Trend in Global-Mean Surface Temperature. Climate Dynamics. 37(3-4): 759-773.

Torres, M. E., et al. 2011. A Complete Ensemble Empirical Mode Decomposition with Adaptive Noise. Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on. IEEE.

Antico, A., Schlotthauer, G., and Torres, M. 2014. Analysis of Hydroclimatic Variability and Trends Using a Novel Empirical Mode Decomposition: Application to the Paraná River Basin. Journal of Geophysical Research: Atmospheres. 119(3): 1218-1233.

Montgomery, D. C., Jennings, C. L., and Kulahci, M. 2015. Introduction to Time Series Analysis and Forecasting. John Wiley & Sons.

Box, G. E., Jenkins, G. M., and Reinsel, G. C. 2011. Time Series Analysis: Forecasting and Control. Vol. 734. John Wiley & Sons.

Brocklebank, J. and Dickey, D. A. 2003. SAS for Forecasting Time Series. Vol. 2. SAS institute.

Hamilton, J. D. 1994. Time Series Analysis. Vol. 2. Princeton university Press Princeton.

Saini, N. and Mittal, A. K. 2014. Forecasting Volatility in Indian Stock Market using State Space Models. Journal of Statistical and Econometric Methods. 3(1): 115-136.

Huang, N. E., et al. 1998. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. The Royal Society.

Huang, N. E., Shen, Z., and Long, S. R. 1999. A New View of Nonlinear Water Waves: The Hilbert Spectrum 1. Annual Review of Fluid Mechanics. 31(1): 417-457.

Colominas, M. A., et al. 2012. Noise-assisted EMD Methods in Action. Advances in Adaptive Data Analysis. 4(04): 1250025.

Yu, L., Wang, S., and Lai, K. K. 2005. A Novel Nonlinear Ensemble Forecasting Model Incorporating GLAR and ANN for Foreign Exchange Rates. Computers & Operations Research. 32(10): 2523-2541.

Yu, L., Wang, S., and Lai, K. K. 2010. Foreign-Exchange-Rate Forecasting with Artificial Neural Networks. Vol. 107. Springer Science & Business Media.

Petris, G. 2010. An R Package for Dynamic Linear Models. Journal of Statistical Software. 36(12): 1-16.

Akaike, H. 1974. A New Look at the Statistical Model Identification. Automatic Control, IEEE Transactions on. 19(6): 716-723.

DOI: https://doi.org/10.11113/jt.v80.10852

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