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Articles

A second-order fuzzy time series model for stock price analysis

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Pages 2514-2526 | Received 13 Oct 2018, Accepted 23 Mar 2019, Published online: 05 Apr 2019

References

  • J. Aldrich, Correlations genuine and spurious in Pearson and Yule, Stat. Sci. 10 (1995), pp. 364–376. doi: 10.1214/ss/1177009870
  • G.E.P. Box, G.M. Jenkins, and G.C. Reinsel, Time series analysis: forecasting and control, Oper. Res. Soc. 22 (1976), pp. 199–201.
  • S.S.L. Chang and L.A. Zadeh, On fuzzy mapping and control, IEEE Trans. Syst. Man Cyber. – Syst. 2 (1972), pp. 30–34. doi: 10.1109/ICSMC.2002.1173380
  • M.Y. Chen, A high-order fuzzy time series forecasting model for internet stock trading, Future Gener. Comput. Syst. 37 (2014), pp. 461–467. doi: 10.1016/j.future.2013.09.025
  • S.M. Chen, Forecasting enrollments based on fuzzy time series, Fuzzy Sets Syst. 81 (1996), pp. 311–319. doi: 10.1016/0165-0114(95)00220-0
  • S.M. Chen, Forecasting enrollments based on high-order fuzzy time series, Cybern. Syst. 33 (2002), pp. 1–16. doi: 10.1080/019697202753306479
  • M.Y. Chen and B.T. Chen, A hybrid fuzzy time series model based on granular computing for stock price forecasting, Inform. Sci. 294 (2015), pp. 227–241. doi: 10.1016/j.ins.2014.09.038
  • M.Y. Chen and B.T. Chen, Online fuzzy time series analysis based on entropy discretization and a fast fourier transform, Appl. Soft Comput. 14 (2014), pp. 156–166. doi: 10.1016/j.asoc.2013.07.024
  • K.A. Chrysafis, B.K. Papadopoulos, and G. Papaschinopoulos, On the fuzzy difference equations of finance, Fuzzy Set Syst. 159 (2008), pp. 3259–3270. doi: 10.1016/j.fss.2008.06.007
  • D. Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications, Academic Press, New York, NY, 1980.
  • P.Y. Ekel and F.H.S. Neto, Algorithms of discrete optimization and their application to problems with fuzzy coefficients, Expert Syst. Appl. 176 (2006), pp. 2846–2868.
  • H.Y. Guo, P. Witold and X.D. Liu, Fuzzy time series forecasting based on axiomatic fuzzy set theory, Neural Comput. Appl. 26 (2018), pp. 2807–2817.
  • T.J. Hsieh, H.F. Hsiao and W.C. Yeh, Forecasting stock markets using wavelet transforms and recurrent neural networks: an integrated system based on artificial bee colony algorithm, Appl. Soft Comput. 11 (2011), pp. 2510–2525. doi: 10.1016/j.asoc.2010.09.007
  • K.H. Huamg, Effective lengths of intervals to improve forecasting fuzzy time series, Fuzzy Set Syst. 123 (2001), pp. 387–394. doi: 10.1016/S0165-0114(00)00057-9
  • C.S. Lee and M.H. Wang, Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition, Expert Syst. Appl. 33 (2007), pp. 606–619. doi: 10.1016/j.eswa.2006.06.006
  • W. Lu, X.Y. Chen, W. Pedrycz, X.D. Liu and J.H. Yang, Using interval information granules to improve forecasting in fuzzy time series, Int. J. Approx. Reason. 57 (2015), pp. 1–18. doi: 10.1016/j.ijar.2014.11.002
  • M. Panigrahi, G. Panda and S. Nanda, Convex fuzzy mapping with differentiability and its application in fuzzy optimization, Eur. J. Oper. Res. 185 (2008), pp. 47–62. doi: 10.1016/j.ejor.2006.12.053
  • A. Saeidifar and E. Pasha, The possibilistic moments of fuzzy numbers and their applications, J. Comput. Appl. Math. 223 (2009), pp. 1028–1042. doi: 10.1016/j.cam.2008.03.045
  • P. Singh, High-order fuzzy-neuro-entropy integration-based expert system for time series, Neural Comput. Appl. 28 (2017), pp. 3851–3868. doi: 10.1007/s00521-016-2261-4
  • P. Singh and B. Borah, High-order fuzzy-neuro expert system for time series forecasting, Knowl. Based Syst. 46 (2013), pp. 12–21. doi: 10.1016/j.knosys.2013.01.030
  • Q. Song and B.S. Chissom, Forecasting enrollments with fuzzy time series - part I, Fuzzy Sets Syst. 54 (1993), pp. 1–9. doi: 10.1016/0165-0114(93)90355-L
  • Student, The elimination of spurious correlation due to position in time or space, Biometrika 10 (1914), pp. 26–279.
  • N.Y. Wang and S.M. Chen, Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factors highorder fuzzy time series, Expert Syst. Appl. 36 (2009), pp. 2143–2154. doi: 10.1016/j.eswa.2007.12.013
  • N.Y. Wang, S.M. Chen and J.S. Pan, Forecasting enrollments based on automatic clustering techniques and fuzzy time series. Proceedings of 12th Conference on Artificial Intelligence and Applications, Yunlin, Taiwan, 2007.
  • L.Z. Wang, X.D. Liu and W. Pedrycz, Determination of temporal information granules to improve forecasting in fuzzy time series, Expert Syst. Appl. 41 (2014), pp. 3134–3142. doi: 10.1016/j.eswa.2013.10.046
  • J. Xu, Q. Liu and R. Wang, A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor, Inform. Sci. 179 (2008), pp. 2022–2043. doi: 10.1016/j.ins.2007.11.025
  • R.R. Yager, On ordered weighted averaging aggregation operators in multicriteria decision making, IEEE Trans. Syst. Man Cybern. 18 (1986), pp. 183–190. doi: 10.1109/21.87068
  • H.K. Yu, Weighted fuzzy time series models for TAIEX forecasting, Physica A 349 (2005), pp. 609–624. doi: 10.1016/j.physa.2004.11.006
  • L.A. Zadeh, Fuzzy sets, Inf. Control 8 (1965), pp. 338–353. doi: 10.1016/S0019-9958(65)90241-X

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