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An Intelligent and Hybrid Weighted Fuzzy Time Series Model Based on Empirical Mode Decomposition for Financial Markets Forecasting

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Advances in Data Mining. Applications and Theoretical Aspects (ICDM 2018)

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Abstract

Given the potentially high impact of accurate financial market forecasting, there has been considerable research on time series analysis for financial markets. We present a new Intelligent Hybrid Weighted Fuzzy (IHWF) time series model to improve forecasting accuracy in financial markets, which are complex nonlinear time-sensitive systems, influenced by many factors. The IHWF model uniquely combines Empirical Mode Decomposition (EMD) with a novel weighted fuzzy time series method. The model is enhanced by an Adaptive Sine-Cosine Human Learning Optimization (ASCHLO) algorithm to help find optimal parameters that further improve forecasting performance. EMD is a time series processing technique to extract the possible modes of various kinds of institutional and individual investors and traders, embedded in a given time series. Subsequently, the proposed weighted fuzzy time series method with chronological order based frequency and Neighborhood Volatility Direction (NVD) is analyzed and integrated with ASCHLO to determine the effective universe discourse, intervals and weights. In order to evaluate the performance of proposed model, we evaluate actual trading data of Taiwan Capitalization Weighted Stock Index (TAIEX) from 1990 to 2004 and the findings are compared with other well-known forecasting models. The results show that the proposed method outperforms the listing models in terms of accuracy.

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References

  1. Abu-Mostafa, Y.S., Atiya, A.F.: Introduction to financial forecasting. Appl. Intell. 6(3), 205–213 (1996)

    Article  Google Scholar 

  2. Alfonso, G., de Hierro, A.R.L., Roldán, C.: A fuzzy regression model based on finite fuzzy numbers and its application to real-world financial data. J. Comput. Appl. Math. 318, 47–58 (2017)

    Article  MathSciNet  Google Scholar 

  3. Arrow, K.J., Kruz, M.: Public Investment, the Rate of Return, and Optimal Fiscal Policy, vol. 1. Routledge, Abingdon (2013)

    Google Scholar 

  4. Bilias, Y., Georgarakos, D., Haliassos, M.: Portfolio inertia and stock market fluctuations. J. Money Credit Bank. 42(4), 715–742 (2010)

    Article  Google Scholar 

  5. Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2015)

    MATH  Google Scholar 

  6. Cai, Q., Zhang, D., Zheng, W., Leung, S.C.: A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression. Knowl.-Based Syst. 74, 61–68 (2015)

    Article  Google Scholar 

  7. Cao, J., Yan, Z., He, G.: Application of multi-objective human learning optimization method to solve AC/DC multi-objective optimal power flow problem. Int. J. Emerg. Electr. Power Syst. 17(3), 327–337 (2016)

    Google Scholar 

  8. Chen, M.Y., Chen, B.T.: A hybrid fuzzy time series model based on granular computing for stock price forecasting. Inf. Sci. 294, 227–241 (2015)

    Article  MathSciNet  Google Scholar 

  9. Chen, S.: Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 81(3), 311–319 (1996)

    Article  Google Scholar 

  10. Chen, S.M., Chang, Y.C.: Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques. Inf. Sci. 180(24), 4772–4783 (2010)

    Article  MathSciNet  Google Scholar 

  11. Chen, S.M., Chen, C.D.: TAIEX forecasting based on fuzzy time series and fuzzy variation groups. IEEE Trans. Fuzzy Syst. 19(1), 1–12 (2011)

    Article  Google Scholar 

  12. Chen, S.M., Phuong, B.D.H.: Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors. Knowl.-Based Syst. 118, 204–216 (2017)

    Article  Google Scholar 

  13. Cheng, S.H., Chen, S.M., Jian, W.S.: Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures. Inf. Sci. 327, 272–287 (2016)

    Article  MathSciNet  Google Scholar 

  14. Egrioglu, E., Aladag, C., Yolcu, U., Uslu, V.R., Erilli, N.A.: Fuzzy time series forecasting method based on Gustafson-kessel fuzzy clustering. Expert Syst. Appl. 38(8), 10355–10357 (2011)

    Article  Google Scholar 

  15. Guo, X., McAleer, M., Wong, W.K., Zhu, L.: A Bayesian approach to excess volatility, short-term underreaction and long-term overreaction during financial crises. Technical report, Tinbergen Institute Discussion Paper (2016)

    Google Scholar 

  16. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H., Zheng, Q., Yen, N., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 454, pp. 903–995. The Royal Society (1998)

    Article  MathSciNet  Google Scholar 

  17. Huarng, K., Yu, T.H.K.: Ratio-based lengths of intervals to improve fuzzy time series forecasting. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 36(2), 328–340 (2006)

    Article  Google Scholar 

  18. Lee, L.W., Wang, L.H., Chen, S.M., Leu, Y.H.: Handling forecasting problems based on two-factors high-order fuzzy time series. IEEE Trans. Fuzzy Syst. 14(3), 468–477 (2006)

    Article  Google Scholar 

  19. Li, C., Chiang, T.W.: Complex neurofuzzy ARIMA forecasting - a new approach using complex fuzzy sets. IEEE Trans. Fuzzy Syst. 21(3), 567–584 (2013)

    Article  Google Scholar 

  20. McCall, J.: Genetic algorithms for modelling and optimisation. J. Comput. Appl. Math. 184(1), 205–222 (2005)

    Article  MathSciNet  Google Scholar 

  21. Menkhoff, L., Schmeling, M., Schmidt, U.: Overconfidence, experience, and professionalism: an experimental study. J. Econ. Behav. Organ. 86, 92–101 (2013)

    Article  Google Scholar 

  22. Ravi, K., Vad, R., Prasad, P.S.R.K.: Fuzzy formal concept analysis based opinion mining for CRM in financial services. Appl. Soft Comput. 60, 786–807 (2017)

    Article  Google Scholar 

  23. Rubio, A., Bermúdez, J.D., Vercher, E.: Improving stock index forecasts by using a new weighted fuzzy-trend time series method. Expert Syst. Appl. 76, 12–20 (2017)

    Article  Google Scholar 

  24. Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series-part i. Fuzzy Sets Syst. 54(1), 1–9 (1993)

    Article  Google Scholar 

  25. Talarposhti, F.M., Sadaei, H.J., Enayatifar, R., Guimarães, F.G., Mahmud, M., Eslami, T.: Stock market forecasting by using a hybrid model of exponential fuzzy time series. Int. J. Approx. Reason. 70, 79–98 (2016)

    Article  MathSciNet  Google Scholar 

  26. Teoh, H.J., Chen, T.L., Cheng, C.H., Chu, H.H.: A hybrid multi-order fuzzy time series for forecasting stock markets. Expert Syst. Appl. 36(4), 7888–7897 (2009)

    Article  Google Scholar 

  27. Wang, L., Ni, H., Yang, R., Fei, M., Ye, W.: A simple human learning optimization algorithm. In: Fei, M., Peng, C., Su, Z., Song, Y., Han, Q. (eds.) LSMS/ICSEE 2014. CCIS, vol. 462, pp. 56–65. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45261-5_7

    Chapter  Google Scholar 

  28. Wang, L., Ni, H., Yang, R., Pappu, V., Fenn, M.B., Pardalos, P.M.: Feature selection based on meta-heuristics for biomedicine. Optim. Methods Softw. 29(4), 703–719 (2014)

    Article  MathSciNet  Google Scholar 

  29. Wang, L., Ni, H., Yang, R., Pardalos, P.M., Du, X., Fei, M.: An adaptive simplified human learning optimization algorithm. Inf. Sci. 320, 126–139 (2015)

    Article  MathSciNet  Google Scholar 

  30. Wang, L., Ni, H., Yang, R., Pardalos, P.M., Jia, L., Fei, M.: Intelligent virtual reference feedback tuning and its application to heat treatment electric furnace control. Eng. Appl. Artif. Intell. 46, 1–9 (2015)

    Article  Google Scholar 

  31. Wang, L., Yang, R., Ni, H., Ye, W., Fei, M., Pardalos, P.M.: A human learning optimization algorithm and its application to multi-dimensional knapsack problems. Appl. Soft Comput. 34, 736–743 (2015)

    Article  Google Scholar 

  32. Wang, L., Yang, R., Pardalos, P.M., Qian, L., Fei, M.: An adaptive fuzzy controller based on harmony search and its application to power plant control. Int. J. Electr. Power Energy Syst. 53, 272–278 (2013)

    Article  Google Scholar 

  33. Wang, L., Yang, R., Xu, Y., Niu, Q., Pardalos, P.M., Fei, M.: An improved adaptive binary harmony search algorithm. Inf. Sci. 232, 58–87 (2013)

    Article  MathSciNet  Google Scholar 

  34. Wang, L., Liu, X., Pedrycz, W.: Effective intervals determined by information granules to improve forecasting in fuzzy time series. Expert Syst. Appl. 40(14), 5673–5679 (2013)

    Article  Google Scholar 

  35. Yang, R., Xu, M., He, J., Ranshous, S., Samatova, N.F.: An intelligent weighted fuzzy time series model based on a sine-cosine adaptive human learning optimization algorithm and its application to financial markets forecasting. In: Cong, G., Peng, W.-C., Zhang, W.E., Li, C., Sun, A. (eds.) ADMA 2017. LNCS (LNAI), vol. 10604, pp. 595–607. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69179-4_42

    Chapter  Google Scholar 

  36. Yu, H.K.: Weighted fuzzy time series models for TAIEX forecasting. Phys. A 349(3), 609–624 (2005)

    Article  Google Scholar 

  37. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  Google Scholar 

  38. Zeng, X., Li, Y., Leng, S., Lin, Z., Liu, X.: Investment behavior prediction in heterogeneous information network. Neurocomputing 217, 125–132 (2016)

    Article  Google Scholar 

  39. Zhang, W., Zhang, S., Zhang, S., Yu, D., Huang, N.: A multi-factor and high-order stock forecast model based on type-2 FTS using cuckoo search and self-adaptive harmony search. Neurocomputing 240, 13–24 (2017)

    Article  Google Scholar 

  40. Zhong, X., Enke, D.: Forecasting daily stock market return using dimensionality reduction. Expert Syst. Appl. 67, 126–139 (2017)

    Article  Google Scholar 

  41. Zhou, R., Yang, Z., Yu, M., Ralescu, D.A.: A portfolio optimization model based on information entropy and fuzzy time series. Fuzzy Optim. Decis. Mak. 14(4), 381 (2015)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This material is based upon work supported in whole or in part with funding from the Laboratory for Analytic Sciences (LAS). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the LAS and/or any agency or entity of the United States Government.

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Authors and Affiliations

  1. North Carolina State University, Raleigh, NC, USA

    Ruixin Yang, Mingyang Xu, Haoqi Ni, Paul Jones & Nagiza Samatova

  2. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Nagiza Samatova

  3. Shanghai University, Shanghai, China

    Junyi He

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  1. Ruixin Yang
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  4. Haoqi Ni
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Correspondence to Ruixin Yang .

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  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

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Yang, R., He, J., Xu, M., Ni, H., Jones, P., Samatova, N. (2018). An Intelligent and Hybrid Weighted Fuzzy Time Series Model Based on Empirical Mode Decomposition for Financial Markets Forecasting. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2018. Lecture Notes in Computer Science(), vol 10933. Springer, Cham. https://doi.org/10.1007/978-3-319-95786-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-95786-9_8

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