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Soft Computing Technologies in Business and Economic Forecasting

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Book cover Soft Computing and its Applications in Business and Economics

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 157))

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Abstract

One of the first explicit uses of neural networks for time series analysis was in 1987 when Lapedes and Farber demonstrated that feed-forward neural networks could be used for modeling deterministic chaos [15].

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References

  1. Aliev RA, Aliev RR (2001) Soft Computing and its Application. World Scientific, New Jersey, London, Singapure, Hong Kong

    Google Scholar 

  2. Aliev RA, Shachnazarov MM, Gulko DE (1988) Scheduling expert systems (in English and Russian). J News of Academy of Sciences of USSR, Tech. Cybernetics 5: 118–128

    Google Scholar 

  3. Aliev RA, Shakhnazarov MM, Abdikeev NM (1990) Intelligent control systems. Moscow: Radio I Svyaz (in Russian)

    Google Scholar 

  4. Bojadziev G, Bojadziev M (1997) Fuzzy logic for business, finance, and management. World Scientific

    Google Scholar 

  5. Buckley JJ, Hayashi Y (1998) Application of fuzzy chaos to fuzzy simulation. J Fuzzy Sets and Systems ‘89: 151–157

    Google Scholar 

  6. Chen SM (1996) Forecasting enrollments based on fuzzy time series. J Fuzzy Sets and Systems 81: 311–319

    Article  Google Scholar 

  7. Chao-Chih Tsai, Shun-Jyh Wu (2000) Forecasting enrollments with high-order fuzzy time series. In: IEEE 19“ International Conference of the North American Fuzzy Information Processing Society, pp 196–200

    Google Scholar 

  8. Deboeck G (ed) (1994) Trading on the Edge. NY: John Wiley and Sons

    Google Scholar 

  9. Fazlollahi B, Aliev R (1998) Multi-agent systems in finance. In: Third Inter. Conf. on Application of Fuzzy Systems and Soft Computing. Wiesbaden, Germany, pp 65–62

    Google Scholar 

  10. Fazlollahi B, Aliev RR (2000) Forecasting time series system with fuzzy decision maker. In: World Conference on Intelligent Systems for Industrial Automation WCIS’2000. Tashkent, Uzbekistan, pp 111–114

    Google Scholar 

  11. Hwang JR, Chen S.M, Lee CH (1996) A new method for handling forecasting problems based on fuzzy time series. In: 7`in Internat. Conf. On Information Management. Chungli, Taoyuan, Taiwan, ROC, pp 312–321

    Google Scholar 

  12. Hwang JR, Chen ShM, Lee ChH (1998) Handling forecasting problems using fuzzy time series. J Fuzzy Sets and Systems 100: 217–228

    Article  Google Scholar 

  13. Ishikawa A, Amagasa M, Shiga T, Tomizawa G, Tatsuta R, Mieno H (1993) The max-min Delphi method and fuzzy Delphi method via fuzzy integration. J Fuzzy Sets and Systems 55: 241–253

    Article  Google Scholar 

  14. Jang JR, Sun C (1993) Predicting chaotic time series with fuzzy IF-THEN rules. In: Second IEEE Inter. Cons. Fuzzy systems. San Francisco, CA, Mar., vol 2, pp 1079–1084

    Google Scholar 

  15. Jason Kingdon (1998) Intelligent Systems and Financial Forecasting. Springer

    Google Scholar 

  16. Jeng-Ren Hwang, Shyi-Ming Chen, Chia-Hoang Lee (1998) Handling forecasting problems using fuzzy time series. J Fuzzy Sets and Systems 100: 217–228

    Article  Google Scholar 

  17. Kaufmann A, Gupta (1988) MM Fuzzy Mathematical Models in Engineering and Management Science. North-Holland, Amsterdam

    Google Scholar 

  18. Kim D, Kim Ch (1997) Forecasting Time Series with Genetic Fuzzy Predictor Ensemble. J IEEE Transaction on Fuzzy Systems vol 5 (4): 523–535

    Article  Google Scholar 

  19. Kuo RJ (1998) A decision support system for the stock market through integration of fuzzy neural networks and fuzzy Delphi. J Applied Artificial Intelligence 12: 501–520

    Article  Google Scholar 

  20. Li Zuoyoung, Chen Zhenpei, Li Jitao (1988) A model of weather forecast by fuzzy grade statistics. J Fuzzy Sets and Systems 26: 275–281

    Article  Google Scholar 

  21. Mohamad H Hassoun (1995) Fundamentals of Artificial Neural Networks. A Bradford Book

    Google Scholar 

  22. Murray TJ, Pipino LL, JP van Gigch, (1985) A pilot study of fuzzy set modification of Delphi. J Human Systems Mgmt. 5: 76–80

    Google Scholar 

  23. Oscar Castillo, Patricia Melin (2001) A New-Fractal Approach for Forecasting Financial and Economic Time Series. J IEEE: 929–934

    Google Scholar 

  24. Petridis V, Kehagias A (1997) Predictive modular fuzzy systems for time-series classification. J IEEE Transactions on Fuzzy Systems vol 5 (3): 381–397

    Article  Google Scholar 

  25. Ping-Teng Chang, Liang-Chih Huang, Horng-Jiun Lin (2000) The fuzzy Delphi method via fuzzy statistics and membership function fitting and an application to the human resources. J Fuzzy Sets and Systems 112: 511–520

    Article  Google Scholar 

  26. Richard B, Chace Nicholas, Aquuilano J, F Robert Jacobs (1998) Production and Operations Management. Manufacturing and Services. Irwin/McGrow-Hill

    Google Scholar 

  27. Song Q, Chissom BS (1993) Forecasting enrollments with fuzzy time series — part I. J Fuzzy Sets and Systems 54: 1–9

    Article  MathSciNet  Google Scholar 

  28. Song Q, Chissom BS (1994) Forecasting enrollments with fuzzy time series–part II. J Fuzzy Sets and Systems 62: 1–8

    Article  Google Scholar 

  29. Song Q, Chissom BS (1993) Fuzzy time series and its models. J Fuzzy Sets and Systems 54: 269–277

    Article  MathSciNet  MATH  Google Scholar 

  30. Sullivan J, Woodall WI-I (1994) A comparison of fuzzy forecasting and Markov modeling. J Fuzzy Sets and Systems 64: 279–293

    Article  Google Scholar 

  31. Tsai CC, Wu SJ (1999) A Study for Second-order Modeling of Fuzzy Time Series. In: IEEE International Fuzzy Systems Conference. Seoul, Korea

    Google Scholar 

  32. Zadeh L (1975) The concept of a linguistic variable and its application to approximate reasoning. J Information Sciences 8: 43–80

    Article  Google Scholar 

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Aliev, R.A., Fazlollahi, B., Aliev, R.R. (2004). Soft Computing Technologies in Business and Economic Forecasting. In: Soft Computing and its Applications in Business and Economics. Studies in Fuzziness and Soft Computing, vol 157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44429-9_4

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  • DOI: https://doi.org/10.1007/978-3-540-44429-9_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53588-8

  • Online ISBN: 978-3-540-44429-9

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