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A big data framework for stock price forecasting using fuzzy time series

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Abstract

Stock price forecasting is the most difficult field owing to irregularities. Therefore, the stock price forecasting and recommendation is an extremely challenging task. In this paper, a big data framework for stock price forecasting using fuzzy time series is proposed in user-friendly form. The method fully capitalizes on the two key technologies, fuzzy set theory and classical time series forecasting methods, to deal with the stock price forecasting. First, using the fuzzy time series the method predicts the fuzzy trend of the forecasted data based on historical stock big data. Then, an autoregressive model is utilized to determine the fluctuation quantity of the forecasted data. Finally, the forecasted stock price is obtained by integrating trend prediction with fluctuation quantity. TAIEX are employed to illustrate the proposed forecasting framework and to compare the forecasting accuracy between the proposed forecasting framework and the existing methods. The experimental results indicate that the proposed forecasting framework produces better forecasting performance.

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References

  1. Aladag CH, Basaran MA, Egrioglu E, Yolcu U, Uslu VR (2008) Forecasting in high order fuzzy time series by using neural networks to define fuzzy relations. Expert Syst Appl 36(3):4228–4231

    Article  Google Scholar 

  2. Chen SM (1996) Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst 81:311–319

    Article  Google Scholar 

  3. Chen SM (2002) Forecasting enrollments based on high-order fuzzy time series. Cybern Syst 33:1–16

    Article  MATH  Google Scholar 

  4. Chen SM, Chen CD (2011) TAIEX Forecasting based on fuzzy time series and fuzzy variation groups. IEEE Trans Fuzzy Syst 19(1):1–12

    Article  Google Scholar 

  5. Chen SM, Hwang JR (2000) Temperature prediction using fuzzy time series. IEEE Trans Syst Man Cybern Part B Cybern 30(2):263–275

    Article  Google Scholar 

  6. Chen SM, Wang NY (2010) Fuzzy forecasting based on fuzzy-trend logical relationship groups. IEEE Trans Syst Man Cybern Part B Cybern 40(5):1343–1358

    Article  Google Scholar 

  7. Chu HH, Chen TL, Cheng CH, Huang CC (2009) Fuzzy dual-factor timeseries for stock index forecasting. Expert Syst Appl 36:165–171

    Article  Google Scholar 

  8. Huarng K (2001) Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets Syst 123(3):387–394

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  10. Jilani TA, Burney SMA (2008) A refined fuzzy time series model for stock market forecasting. Physica A 387:2857–2862

    Article  Google Scholar 

  11. Jilani TA, Burney SMA (2008) Multivariate stochastic fuzzy forecasting models. Expert Syst Appl 35:691–700

    Article  Google Scholar 

  12. Lee LW, Wang LH, Chen SM, Leu YH (2006) Handling forecasting problems based on two-factors high-order fuzzy time series. IEEE Trans Fuzzy Syst 14(3):468–477

    Article  Google Scholar 

  13. Lee LW, Wang LH, Chen S-M (2007) Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms. Expert Syst Appl 33:539–550

    Article  Google Scholar 

  14. Li ST, Cheng YC, Lin SY (2008) A FCM-based deterministic forecasting model for fuzzy time series. Comput Math Appl 56:3052–3063

    Article  MathSciNet  MATH  Google Scholar 

  15. Liu HT, Wei ML (2009) A distance-based fuzzy time series model for exchange rates forecasting. Expert Syst Appl 36:8107–8114

    Article  Google Scholar 

  16. Singh SR (2009) A computational method of forecasting based on high-order fuzzy time series. Expert Syst Appl 36:10551–10559

    Article  Google Scholar 

  17. Song Q (2014) On a new framework of autoregressive fuzzy time series models. Industrial Engineeering & Management Systems 13(4):357–368

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  20. Song Q, Chissom BS (1994) Forecasting enrollments with fuzzy time series–Part II. Fuzzy Sets Syst 64(1):1–8

    Article  Google Scholar 

  21. Sullivan J, Woodall WH (1994) A comparison of fuzzy forecasting and Markov modeling. Fuzzy Sets Syst 64(3):279–293

    Article  Google Scholar 

  22. Teoh HJ, Chen TL, Cheng CH, Chu HH (2009) A hybrid multi-order fuzzy time series for forecasting stock markets. Expert Syst Appl 36:7888–7897

    Article  Google Scholar 

  23. Tsaur RC, Yang JCO, Wang HF (2005) Fuzzy relation analysis in fuzzy time series model. Comput Math Appl 49:539–548

    Article  MathSciNet  MATH  Google Scholar 

  24. Wang NY, Chen SM (2009) Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factors high-order fuzzy time series. Expert Syst Appl 36:2143–2154

    Article  Google Scholar 

  25. Yu HK (2001) Weighted fuzzy time-series models for TAIEX forecasting. Physica A: Statistical Mechanics and its Applications 349(3/4):609–624

    Google Scholar 

Download references

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Authors and Affiliations

  1. School of Science, Jilin Institute of Chemical Technology, Jilin, China

    Weina Wang

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  1. Weina Wang
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Correspondence to Weina Wang.

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Wang, W. A big data framework for stock price forecasting using fuzzy time series. Multimed Tools Appl 77, 10123–10134 (2018). https://doi.org/10.1007/s11042-017-5144-5

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  • DOI: https://doi.org/10.1007/s11042-017-5144-5

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