A compressed sensing based AI learning paradigm for crude oil price forecasting

Energy Economics

Volumn 46, Issue , 2014, Pages 236-245

  Yu, Lean ^a   Zhao, Yang ^a   Tang, Ling ^a  

^a BEIJING UNIVERSITY OF CHEMICAL TECHNOLOGY   (China)

Author keywords

Compressed sensing; Crude oil price prediction; Data denoising; Feed forward neural network; Hybrid model

Indexed keywords

CRUDE OIL PRICE FORECASTING; CRUDE OIL PRICE PREDICTION; DATA DE-NOISING; HYBRID MODEL; LEARNING PARADIGMS;

COMPRESSED SENSING;

ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CRUDE OIL; FORECASTING METHOD; NUMERICAL MODEL; PRICE DETERMINATION;

EID: 84908020500     PISSN: 01409883     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eneco.2014.09.019     Document Type: Article

References (51)

1. Ahmed N., Natarajan T., Rao K.R. Discrete cosine transform. IEEE Trans. Comput. 1974, 100(1):90-93.
2. Amin-Naseri M.R., Gharacheh E.A. A hybrid artificial intelligence approach to monthly forecasting of crude oil price time series. The Proceedings of the 10th International Conference on Engineering Applications of Neural Networks, CEUR-WS284 2007, 160-167.
3. Bao Y., Xiong T., Hu Z. Multi-step-ahead time series prediction using multiple-output support vector regression. Neurocomputing 2014, 129:482-493.
4. Bao Y., Xiong T., Hu Z. PSO-MISMO modeling strategy for multi-step-ahead time series prediction. IEEE Trans. Cybern. 2014, 44(5):655-668.
5. Baxter M., King R.G. Measuring business cycles: approximate band-pass filters for economic time series. Rev. Econ. Stat. 1999, 81(4):575-593.
6. Bowerman B.L., O'Connell R.T. Time Series and Forecasting: An Applied Approach 1993, Duxbury Press, California.
7. Box G.E.P., Jenkins G.M. Time Series Analysis: Forecasting and Control 1970, Holden Day Press, San Francisco, CA.
8. Bracewell R.N. Fourier Transform and Its applications 1980, McGraw-Hill Press, New York.
9. Candès E.J., Wakin M.B. An introduction to compressive sampling. IEEE Signal Process. Mag. 2008, 25(2):21-30.
10. Chen B.T., Chen M.Y., Fan M.H., Chen C.C. Forecasting stock price based on fuzzy time-series with equal-frequency partitioning and fast Fourier transform algorithm. Computing, Communications and Applications Conference (ComComAp), IEEE 2012, 238-243.
11. Diebold F.X., Mariano R.S. Comparing predictive accuracy. J. Bus. Econ. Stat. 2002, 20(1):134-144.
12. Donoho D.L. De-noising by soft-thresholding. IEEE Trans. Inf. Theory 1995, 41(3):613-627.
13. Eldar Y.C., Kutyniok G. Compressed Sensing: Theory and Applications 2012, Cambridge University Press, Cambridge.
14. Faria E.L., Albuquerque M.P., Gonzalez J.L., Cavalcante J.T.P., Albuquerque M.P. Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods. Expert Syst. Appl. 2009, 36(10):12506-12509.
15. Gardner E.S. Exponential smoothing: the state of the art. J. Forecast. 1985, 4(1):1-28.
16. Hagen R. How is the international price of a particular crude determined?. OPEC Rev. 1994, 18(1):127-135.
17. Han B., Xiong J., Li L., Yang J., Wang Z. Research on millimeter-wave image denoising method based on contourlet and compressed sensing. Signal Processing Systems (ICSPS), 2010 2nd International Conference on IEEE 2010, vol. 2. (V2-471 - V2-475).
18. He K., Lai K.K., Yen J. A hybrid slantlet denoising least squares support vector regression model for exchange rate prediction. Procedia Comput. Sci. 2010, 1(1):2397-2405.
19. He K., Lai K.K., Xiang G. Portfolio value at risk estimate for crude oil markets: a multivariate wavelet denoising approach. Energies. 2012, 5(4):1018-1043.
20. Hodrick R.J., Prescott E.C. Postwar US business cycles: an empirical investigation. J. Money, Credit, Bank. 1997, 29(1):1-16.
21. Hornik K., Stinchcombe M., White H. Multilayer feedforward networks are universal approximators. Neural Netw. 1989, 2(5):359-366.
22. Jammazi R., Aloui C. Crude oil price forecasting: experimental evidence from wavelet decomposition and neural network modeling. Energy Econ. 2012, 34(3):828-841.
23. Kalman R.E. A new approach to linear filtering and prediction problems. J. Basic Eng. 1960, 82(1):35-45.
24. Mallat S.G. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 1989, 11(7):674-693.
25. Marim M., Angelini E., Olivo-Marin J.C. Denoising in fluorescence microscopy using compressed sensing with multiple reconstructions and non-local merging. Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE 2010, 3394-3397.
26. Mirmirani S., Li H.C. A comparison of VAR and neural networks with genetic algorithm in forecasting price of oil. Adv. Econ. 2005, 19:203-223.
27. Nasseri M., Moeini A., Tabesh M. Forecasting monthly urban water demand using Extended Kalman Filter and Genetic Programming. Expert Syst. Appl. 2011, 38(6):7387-7395.
28. Sang Y.F. Improved wavelet modeling framework for hydrologic time series forecasting. Water Resour. Manag. 2013, 27(8):2807-2821.
29. Sang Y.F., Wang D., Wu J.C., Zhu Q.P., Wang L. Entropy-based wavelet de-noising method for time series analysis. Entropy 2009, 11(4):1123-1147.
30. Shambora W.E., Rossiter R. Are there exploitable inefficiencies in the futures market for oil?. Energy Econ. 2007, 29(1):18-27.
31. Shumway R.H., Stoffer D.S. An approach to time series smoothing and forecasting using the EM algorithm. J. Time Ser. Anal. 1982, 3(4):253-264.
32. Stevens P. The determination of oil prices 1945-1995: a diagrammatic interpretation. Energy Policy 1995, 23(10):861-870.
33. Struzik Z.R. Wavelet methods in (financial) time-series processing. Physica A 2001, 296(1):307-319.
34. Suykens J.A., Vandewalle J. Least squares support vector machine classifiers. Neural. Process. Lett. 1999, 9(3):293-300.
35. Taieb B.S., Bontempi G., Atiya A.F., Sorjamaa A. A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Syst. Appl. 2012, 39(8):7067-7083.
36. Tang L., Yu L., Wang S., Li J., Wang S. A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting. Appl. Energy 2012, 93:432-443.
37. Tang L., Yu L., Liu F., Xu W. An integrated data characteristic testing scheme for complex time series data exploration. Int. J. Inf. Technol. Decis. Mak. 2013, 12(3):491-521.
38. Tang L., Wang S., He K., Wang S. A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting. Ann. Oper. Res. 2014, 2014:1-22.
39. Tang L., Yu L., He K. A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting. Appl. Energy 2014, 128:1-14.
40. Vapnik V. The nature of statistical learning theory 1995, Springer-Verlag, New York.
41. Wang S., Yu L., Lai K.K. Crude oil price forecasting with TEI@I methodology. J. Syst. Sci. Complex. 2005, 18(2):145-166.
42. Wang S., Yu L., Tang L., Wang S. A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China. Energy 2011, 36(11):6542-6554.
43. Watkins G.C., Plourde A. How volatile are crude oil prices. OPEC Rev. 1994, 18(4):220-245.
44. Welch G., Bishop G. An introduction to the Kalman Filter, Department of Computer Science 1995, University of North Carolina, Tech. Rep., TR-95-041.
45. White H. Connectionist nonparametric regression: multilayer feedforward networks can learn arbitrary mappings. Neural Netw. 1990, 3(5):535-549.
46. Xie W., Yu L., Xu S., Wang S. A new method for crude oil price forecasting based on support vector machines. Computational Science-ICCS 2006 2006, 444-451. Springer, Berlin Heidelberg.
47. Xiong T., Bao Y., Hu Z. Beyond one-step-ahead forecasting: evaluation of alternative multi-step-ahead forecasting models for crude oil prices. Energy Econ. 2013, 40:405-415.
48. Yu L., Lai K.K., Wang S., He K. Oil price forecasting with an EMD-based multiscale neural network learning paradigm. Computational Science-ICCS 2007 2007, 925-932. Springer, Berlin Heidelberg.
49. Yu L., Wang S., Lai K.K. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Econ. 2008, 30(5):2623-2635.
50. Yuan C. Forecasting exchange rates: the multi-state Markov-switching model with smoothing. Int. Rev. Econ. Financ. 2011, 20(2):342-362.
51. Zhu L., Zhu Y., Mao H., Gu M. A new method for sparse signal denoising based on compressed sensing. Knowledge Acquisition and Modeling, 2009. KAM'09. Second International Symposium on IEEE 2009, vol. 1:35-38. (November).