No Access

Forecasting import and export volume with a combined model based on wavelet filtering

Huidian Long

Business School, University of Shanghai for Science & Technology, Shanghai, 200091, P. R. China

School of Economics & Trade Guangdong University of Foreign Studies, Guangzhou, 510420, P. R. China

E-mail Address: lhd1204@163.com

Search for more papers by this author

and

Guangle Yan

Business School, University of Shanghai for Science & Technology, Shanghai, 200091, P. R. China

E-mail Address: glyan2003@sina.com

Search for more papers by this author

https://doi.org/10.1142/S0219691316500181Cited by:3 (Source: Crossref)

Abstract

In this paper, a non-stationary time series prediction method based on wavelet transform is proposed. By wavelet decomposition, the non-stationary time series is decomposed into a low frequency signal and several high frequency signals. The high frequency signals are predicted with auto-regressive integrated moving average (ARIMA) models, and the low frequency is predicted with an improved GM(1,1)-Markov chain combined model based on Taylor approximation. Finally, an improved ARIMA-GM(1,1)-Markov chain combined model is constructed by using wavelet reconstruction. As an example, we use the statistical data of the total import and export volume in China from 2001 to 2014 for a validation of the effectiveness of the combined model.

Keywords:

AMSC: 22E46, 53C35, 57S20

References

1. F. Abramovich, T. C. Bailey and T. Sapatinas, The theory of predictive distributions, The Statistician 49(1) (2000) 1–29. Crossref, Google Scholar
2. M. Aminghafari and J.-M. Poggi, Forecasting time series using wavelets, Internat. J. Wavelets, Multiresolut. Inform. Process. 5(5) (2007) 709–724. Link, Web of Science, Google Scholar
3. A. Antoniadis, X. Brossat, J. Cugliari and J.-M. Poggi, Clustering functional data using wavelets, Internat. J. Wavelets, Multiresolut. Inform. Process. 11(1) (2013) 1–30. Link, Web of Science, Google Scholar
4. E. Avci and D. Avci, A novel approach for digital radio signal classification: Wavelet packet energy multiclass support vector machine, Expert Syst. Appl. 34(3) (2008) 2140–2147. Crossref, Web of Science, Google Scholar
5. S. I. Chang and S. Yadama, Statistical process control for monitoring nonlinear profiles using wavelet filtering and B-spline approximation, Internat. J. Product. Res. 48(4) (2010) 1049–1068. Crossref, Web of Science, Google Scholar
6. G. Chen, Automatic EEG seizure detection using dual-tree complex wavelet-fourier features, Expert Syst. Appl. 41 (2014) 2391–2394. Crossref, Web of Science, Google Scholar
7. X. C. Dang, Z. J. Hao, Y. Li, Z. Y. Lu and Q. Gao, Network traffic forecasting combination model based on wavelet transform and chaos algorithm, Internat. J. Wavelets, Multiresolut. Inform. Process. 12(3) (2014) 182–193. Link, Web of Science, Google Scholar
8. J. Deng, Gray Prediction and Decision (Huazhong University of Science and Technology Press, Wuhan, 1988). Google Scholar
9. R. Guo, A repairable system modeling: Combining gray system theory with interval-valued fuzzy set theory, Int. J. Reliab. Qual. Saf. Eng. 12(3) (2005) 241–266. Link, Google Scholar
10. T. W. Joo and S. B. Kim, Time series forecasting based on wavelet filtering, Expert Syst. Appl. 42 (2015) 3868–3874. Crossref, Web of Science, Google Scholar
11. U. Kimar and V. Jain, Time series models to forecast energy consumption in India, Energy 35(4) (2010) 1709–1716. Crossref, Web of Science, Google Scholar
12. F. Y. Komaki, The theory of predictive distributions, Transactions of the Institute of Electronics Information & Communication Engineers A 83(6) (2000) 612–619. Google Scholar
13. A. Kumar, D. P. Agrawal and S. D. Joshi, Multiresolution forecasting for us retailing using wavelet decompositions, Internat. J. Wavelets, Multiresolut. Inform. Process. 1(4) (2003) 449–463. Link, Google Scholar
14. A. Kumar, D. P. Agrawal and S. D. Joshi, Multiscale neurofuzzy models for forecasting in time series databases, Internat. J. Wavelets, Multiresolut. Inform. Process. 5(6) (2007) 859–878. Link, Web of Science, Google Scholar
15. C. P. Lai, P. C. Chung and V. S. Tseng, A novel two-level clustering method for time series data analysis, Expert Syst. Appl. 37(9) (2010) 6319–6326. Crossref, Web of Science, Google Scholar
16. S. Li, A trend forecast of China’s import and export trade total volume based on the gray system model, Commer. Res. 3(48) (2008) 113–115. Google Scholar
17. G.-D. Li, D. Yamaguchi and M. Nagai, A GM(1,1)-Markov chain combined model with an application to predict the number of Chinese international airlines, Technol. Forecast. Soc. Change 74 (2007) 1465–1481. Crossref, Web of Science, Google Scholar
18. W. Qian and Z. Liu, Traffic accident forecasting based on gray Markov model, China Saf. Sci. 18(3) (2008) 33–36. Google Scholar
19. B. M. Tabak and M. A. Feitosa, Forecasting industrial production in Brazil: Evidence from a wavelet approach, Expert Syst. Appl. 37(9) (2010) 6345–6351. Crossref, Web of Science, Google Scholar
20. L. Tian, A.-Z. Cao, J. Chen and Z.-G. Duan, Application of RBF neural network in forecast of import and export total, Measure. Control Tech. 24(2) (2005) 50–52. Google Scholar
21. W. Vandaele, Applied Time Series and Box–Jenkins Models (Academic Press, 1984), pp. 49–51. Google Scholar
22. K. L. Wen, Grey Systems: Modeling and Prediction (Yang’s Scientific Research Institute, Taiwan, 2004). Google Scholar
23. G. Xu, Statistical Forecasting and Decision-Making, 2nd edn. (Shanghai University of Finance and Economics Press, 2005), pp. 162–165. Google Scholar
24. Z.-Y. Yang, The prediction of the import–export volume of guangdong with outliers, Appl. Statist. Manag. 27(5) (2008) 795–800. Google Scholar
25. K. Zhu, Gray Markov chain model and its application, Syst. Eng. Theory Pract. 2 (1993) 67–70. Google Scholar