350 rub
Journal Radioengineering №7 for 2014 г.
Article in number:
The extraction of knowledge about dependencies time series for the tasks of forecasting
Keywords: fuzzy time series forecasting fuzzy tendency similarity coefficient
Authors:
N.G. Yarushkina - Dr. Sci. (Eng.), Professor, head. Information systems, Ulyanovsk state technical University. E-mail: jng@ulstu.ru
T.V. Afanaseva - Dr. Sci. (Eng.), associate professor, Dept. Information systems, Ulyanovsk state technical University. E-mail: tv.afanasjeva@gmail.com
A.A. Romanov - Ph. D. (Eng.), Dept. Information systems, Ulyanovsk state technical University. E-mail: romanov73@gmail.com
I.A. Timina - post-graduate student, Dept. Information systems, Ulyanovsk state technical University. E-mail: i.timina@ulstu.ru
Abstract:
Often financial indicators of the planning of economic activity characterized by a high degree of similarity. Analysis such parameters based on time series analysis. Analysis for identify groups of similar time series of indicators allows to identify relation for each indicator to the important factors. Applying this analysis is reduced prediction time by simplifyingforecast operations and allow to identify groups of similar processes for obtaining information about the factors that grouping these processes. The paper describes an approach for solving the problems of modeling and forecasting economic time series by identifying the degree of dependence based on correlation and degree of similarity time series.
Pages: 141-146
References

  1. Holt C.C. Forecasting trends and seasonals by exponentially weighted moving averages // O.N.R. Memorandum. Carnegie Inst. of Technology. 1957. № 2.
  2. Kendall M. Time series: Translated from English / ed. by Yu.P. Lukashina. Moscow: Finansy I statistika. 1981. 199 p.
  3. Anderson T. W. Statistical Analysis of Time Series. New York: John Wiley and Sons. Inc. 1971.
  4. Box J. Time series analysis: Forecasting and control: Translated from English / ed. by F. Pisarenko. Moscow: Mir. 1974. 406 p.
  5. Zadeh A. Lotfi. Fuzzy Sets. Information and Control. 1965.
  6. Sabic D.A., Pedrycz W. Evaluation on fuzzy linear regression models // Fuzzy Sets and Systems. 1991. № 23. R. 51-63.
  7. Chen S.M. Forecasting enrollments based on high-order fuzzy time series // Cybernetics and Systems: An International Journal. 2002. № 33. R. 1-16.
  8. Song Q., Chissom B. Fuzzy time series and its models // Fuzzy Sets and Systems. 1993. № 54. R. 269-277.
  9. Afanas'eva T.V., Jarushkina N.G. Nechetkij dinamicheskij proczess s nechetkimi tendencziyami v analize vremenny'x ryadov // Vestnik Rostovskogo gosudarstvennogo universiteta putej soobshheniya. 2011. № 3. S. 7-16.
  10. Perfilieva I., Yarushkina N., Afanaseva T. Relaxed Discrete F-Transform and its Application to the Time Series Analysis / // Da Ruanetal (Eds.): Computational Intelligence. Foundations and Applications (Proc.of the 9th Int. FLINS Conf.). P. 249 -255, World Scientific, Emei, Chengdu, China, 2-4 August, 2010.
  11. Tanaka H., Uejima S., Asai K. Linear regression analysis with fuzzy model // IEEE Transactions on Systems, Man and Cybernetics. 1982. № 12(6). R. 903-907.
  12. Yarushkina N. et al. Time Series Processing and Forecasting using Soft Computing Tools. Lecture Notes in Computer Science. V. 6743. Proceedings of 13th International Conf. RSFDGrC-2011. Springer-Verlag, BerlinHeidelberg. 2011. XIII. P. 155-163.
  13. Perfilieva I., Yarushkina N., Afanasieva T., Igonin A., Romanov A., Shishkina V. Soft computing tools for time series analysis and forecast / Proceedings of the 9th Int. Conf. on Application of Fuzzy Systems and Soft Computing (ICAFS 2010) Eds. R.A. Aliev, K.W. Bonfig, M. Jamshidi, W. Pedrycz, I.B. Turksen, Prague, August 26-27. 2010. VERLAG b- Quadrat Verlag. P. 50-60.
  14. Gregory, Allan W., Hansen, Bruce E. Residual-based tests for cointegration in models with regime shifts // Journal of Econometrics. 1996. V. 70 (1). P. 99-126.
  15. Engle, Robert F., Granger, Clive W.J. Co-integration and error correction: Representation, estimation and testing // Econometrica. 1987. V. 55 (2). P. 251-276.
  16. Granger Clive. Some Properties of Time Series Data and Their Use in Econometric Model Specification // Journal ofEconometrics. V. 16(1). P. 121-130.
  17. Jarushkina N.G. Osnovy' teorii nechetkix i gibridny'x sistem: ucheb. posobie. M.: Finansy' i statistika. 2004. 320 s.
  18. Perfilieva I., Yarushkina N., Afanasieva T., and Romanov A. Time series analysis by soft computing methods // Int. Journal Of General Systems. 2013. V. 42. P. 687-705.
  19. Perfilieva I., Dankov´a M., and Bede B. Towards a Higher Degree F-Transform // Fuzzy Sets and Systems. 2011. V. 180. P. 319.
  20. Timina I.A. Nechetkaya zavisimost' kak metod resheniya zadach intellektual'nogo analiza vremenny'x ryadov // Avtomatizacziya proczessov upravleniya. Ul'yanovsk: NPO «Mars». 2013. № 3(33). S. 39-44.