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An Intelligent Weighted Fuzzy Time Series Model Based on a Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting

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Advanced Data Mining and Applications (ADMA 2017)

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Abstract

Financial forecasting is an extremely challenging task given the complex, nonlinear nature of financial market systems. To overcome this challenge, we present an intelligent weighted fuzzy time series model for financial forecasting, which uses a sine-cosine adaptive human learning optimization (SCHLO) algorithm to search for the optimal parameters for forecasting. New weighted operators that consider frequency based chronological order and stock volume are analyzed, and SCHLO is integrated to determine the effective intervals and weighting factors. Furthermore, a novel short-term trend repair operation is developed to complement the final forecasting process. Finally, the proposed model is applied to four world major trading markets: the Dow Jones Index (DJI), the German Stock Index (DAX), the Japanese Stock Index (NIKKEI), and Taiwan Stock Index (TAIEX). Experimental results show that our model is consistently more accurate than the state-of-the-art baseline methods. The easy implementation and effective forecasting performance suggest our proposed model could be a favorable market application prospect.

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References

  1. 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  MATH  Google Scholar 

  2. Balcilar, M., Gupta, R., Wohar, M.E.: Common cycles and common trends in the stock and oil markets: evidence from more than 150 years of data. Energy Econ. 61, 72–86 (2017)

    Article  Google Scholar 

  3. 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 

  4. 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 

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

    Article  Google Scholar 

  6. Chen, S.M., Chen, C.D.: Handling forecasting problems based on high-order fuzzy logical relationships. Expert Syst. Appl. 38(4), 3857–3864 (2011)

    Article  Google Scholar 

  7. 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 

  8. Cheng, C.H., Chen, T.L., Teoh, H.J., Chiang, C.H.: Fuzzy time-series based on adaptive expectation model for TAIEX forecasting. Expert Syst. Appl. 34(2), 1126–1132 (2008)

    Article  Google Scholar 

  9. García-Crespo, Á., López-Cuadrado, J.L., González-Carrasco, I., Colomo-Palacios, R., Ruiz-Mezcua, B.: SINVLIO: Using semantics and fuzzy logic to provide individual investment portfolio recommendations. Knowl. Based Syst. 27, 103–118 (2012)

    Article  Google Scholar 

  10. Guresen, E., Kayakutlu, G., Daim, T.U.: Using artificial neural network models in stock market index prediction. Expert Syst. Appl. 38(8), 10389–10397 (2011)

    Article  Google Scholar 

  11. Hung, J.C.: Applying a combined fuzzy systems and garch model to adaptively forecast stock market volatility. Appl. Soft Comput. 11(5), 3938–3945 (2011)

    Article  Google Scholar 

  12. Iskyan, K.: China’s stock markets have soared by 1,479% since 2003. Business Insider November 2016, http://www.businessinsider.com/world-stock-market-capitalizations-2016-11

  13. Javedani Sadaei, H., Lee, M.H.: Multilayer stock forecasting model using fuzzy time series. Sci. World J. 2014 (2014)

    Google Scholar 

  14. Marszałek, A., Burczyński, T.: Modeling and forecasting financial time series with ordered fuzzy candlesticks. Inf. Sci. 273, 144–155 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  15. Merh, N.: Stock market forecasting. J. Inf. Technol. Appl. Manage. 19(1), 1–12 (2012)

    Google Scholar 

  16. Ravi, K., Vadlamani, R., Prasad, P.: Fuzzy formal concept analysis based opinion mining for CRM in financial services. Appl. Soft Comput. 58, 35–52 (2017)

    Article  Google Scholar 

  17. 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 

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

    Article  Google Scholar 

  19. Song, Q., Chissom, B.S.: Fuzzy time series and its models. Fuzzy Sets Syst. 54(3), 269–277 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  20. Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series–part II. Fuzzy Sets Syst. 62(1), 1–8 (1994)

    Article  Google Scholar 

  21. Su, C.H., Cheng, C.H.: A hybrid fuzzy time series model based on anfis and integrated nonlinear feature selection method for forecasting stock. Neurocomputing 205, 264–273 (2016)

    Article  Google Scholar 

  22. 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 

  23. Ticknor, J.L.: A bayesian regularized artificial neural network for stock market forecasting. Expert Syst. Appl. 40(14), 5501–5506 (2013)

    Article  Google Scholar 

  24. Uslu, V.R., Bas, E., Yolcu, U., Egrioglu, E.: A fuzzy time series approach based on weights determined by the number of recurrences of fuzzy relations. Swarm Evol. Comput. 15, 19–26 (2014)

    Article  Google Scholar 

  25. Wang, J., Hou, R., Wang, C., Shen, L.: Improved v-support vector regression model based on variable selection and brain storm optimization for stock price forecasting. Appl. Soft Comput. 49, 164–178 (2016)

    Article  Google Scholar 

  26. 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). doi:10.1007/978-3-662-45261-5_7

    Google Scholar 

  27. 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 

  28. 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 

  29. 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 

  30. Wei, L.Y.: A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Appl. Soft Comput. 42, 368–376 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  33. Zhou, R., Yang, Z., Yu, M., Ralescu, D.A.: A portfolio optimization model based on information entropy and fuzzy time series. Fuzzy Optim. Decis. Making 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.

Author information

Authors and Affiliations

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

    Ruixin Yang, Mingyang Xu, Stephen Ranshous & Nagiza F. Samatova

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

    Nagiza F. Samatova

  3. Shanghai University, Shanghai, China

    Junyi He

Authors
  1. Ruixin Yang
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  2. Mingyang Xu
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  3. Junyi He
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  4. Stephen Ranshous
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  5. Nagiza F. Samatova
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Corresponding author

Correspondence to Nagiza F. Samatova .

Editor information

Editors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Gao Cong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

  3. Macquarie University, Sydney, New South Wales, Australia

    Wei Emma Zhang

  4. Wuhan University, Wuhan, China

    Chengliang Li

  5. Nanyang Technological University, Singapore, Singapore

    Aixin Sun

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Yang, R., Xu, M., He, J., Ranshous, S., Samatova, N.F. (2017). 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, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_42

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  • DOI: https://doi.org/10.1007/978-3-319-69179-4_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69178-7

  • Online ISBN: 978-3-319-69179-4

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