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Moving Approximation Transform and Local Trend Associations in Time Series Data Bases

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Perception-based Data Mining and Decision Making in Economics and Finance

Part of the book series: Studies in Computational Intelligence ((SCI,volume 36))

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References

  1. Banco de Información Económica. URL: http://dgcnesyp.inegi.gob.mx/bdine/ bancos.htm

  2. Bastogne T., Noura H., Richard A., Hittinger J.M. (1997). Application of subspace methods to the identification of a winding process. In: Proceedings of the Fourth European Control Conference, Vol. 5, Brussels

    Google Scholar 

  3. Batyrshin I., Herrera-Avelar R., Sheremetov L., Suarez R. (2004). Moving approximations in time series data mining. In: Proceedings of International Conference on Fuzzy Sets and Soft Computing in Economics and Finance, FSSCEF 2004, St. Petersburg, Russia, Vol. I, pp. 62–72

    Google Scholar 

  4. Batyrshin I., Herrera-Avelar R., Sheremetov L., Suarez R. (2004). On qualitative description of time series based on moving approximations. In: Proceedings of International Conference on Fuzzy Sets and Soft Computing in Economics and Finance, FSSCEF 2004, St. Petersburg, Russia, Vol. I, 73–80

    Google Scholar 

  5. Batyrshin I., Herrera-Avelar R., Sheremetov L., Panova A. (2005). Association networks in time series data mining. - NAFIPS 2005. Soft Computing for Real World Applications, Ann Arbor, Michigan, USA, 754–759

    Google Scholar 

  6. Boverman B.L., O’Connell R.T. (1979). Time Series and Forecasting. Duxbury, Massachusetts

    Google Scholar 

  7. Das G., Lin K.I., Mannila H., Renganathan G., Smyth P. (1998). Rule Discovery from Time Series. Knowledge Discovery and Data Mining, 16–22

    Google Scholar 

  8. Economagic.com: Economic Time Series Page, URL: http://economic-charts.com/blsint.htm

  9. Economic Research. Federal Reserve Bank of St. Luis. URL: http:// research.stlouisfed.org/fred2/categories/15/downloaddata

  10. Everitt B.S., Landau S., Leese M. (2001). Cluster Analysis. Fourth Edition. Arnold, London

    Google Scholar 

  11. Friedman, J.H. (1997). Data Mining and Statistics: What's the Connection? URL: http://www-stat.stanford.edu/~jhf/ftp/dm-stat.ps

  12. Keogh, E., Folias, T. (2002). The UCR Time Series Data Mining Archive. [http://www.cs.ucr.edu/~eamonn/TSDMA/index.html], University of California -Computer Science & Engineering Department, Riverside, CA

  13. Keogh, E., Kasetty, S. (2002). On the need for time series data mining benchmarks: A survey and empirical demonstration. In: SIGKDD’02

    Google Scholar 

  14. Keogh, E., Lonardi, S., Ratanamahatana, C.A. (2004). Towards parameter-free data mining. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA

    Google Scholar 

  15. Last, M., Klein, Y., Kandel, A. (2001). Knowledge discovery in time series databases. IEEE Transactions on Systems, Man, and Cybernetics, 31B

    Google Scholar 

  16. Least Squares Fitting. Wolfram Research, Mathworld, URL: http://mathworld.wolfram.com/LeastSquaresFitting.html

  17. Linear regression lines. MarketScreen. URL: http://www.marketscreen.com/help/AtoZ/default.asp?hideHF=&Num=58

  18. Möller-Levet, C.S., Klawonn, F., Cho, K.H., & Wolkenhauer, O. (2003) Fuzzy clustering of short time-series and unevenly distributed sampling points. IDA 2003, 330–340

    Google Scholar 

  19. Sheremetov L., Rocha L., Batyrshin I. (2005) Towards a Multi-agent Dynamic Supply Chain Simulator for Analysis and Decision Support. In NAFIPS 2005. Soft Computing for Real World Applications, Ann Arbor, Michigan, USA, June 22–25, 263–286

    Google Scholar 

  20. Time Series Forecast. MarketScreen. URL: http://www.marketscreen.com/help/AtoZ/default.asp?hideHF=&Num=102

  21. Zadeh, L.A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90, 111–127

    Article  MATH  MathSciNet  Google Scholar 

  22. Zhang, P., Huang, Y., Shekhar, S., & Kumar, V. (2003). Correlation analysis of spatial time series datasets: a filter-and-refine approach. Proc. Seventh Pacific-Asia Conf. Knowledge Discovery Data Mining,(PAKDD’03), Lecture Notes in Artificial Intelligence Vol. 2637, Springer-Verlag, Seoul, Korea, 532–544

    Google Scholar 

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Author information

Authors and Affiliations

  1. Mexican Petroleum Institute, Eje Central Lazaro Cardenas 152, Col. San Bartolo Atepehuacan, 07730, Mexico, Mexico

    Ildar Batyrshin

  2. Mexican Petroleu Institute, Mexico

    Raul Herrera-Avelar

  3. Mexican Petroleum Institute, Eje Central Lazaro Cardenas 152, Col. San Bartolo Atepehuacan, 07730, Mexico, Mexico

    Leonid Sheremetov

  4. Kazan Power Engineering Institute, Russia

    Aleksandra Panova

Authors
  1. Ildar Batyrshin
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  2. Raul Herrera-Avelar
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  3. Leonid Sheremetov
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  4. Aleksandra Panova
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Editor information

Editors and Affiliations

  1. Mexican Petroleum Institute, Eje Central Lazaro Cardenas 152, Col. San Bartolo Atepehuacan, 07730, Mexico, Mexico

    Ildar Batyrshin  & Leonid Sheremetov  & 

  2. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447, Warszawa, Poland

    Janusz Kacprzyk

  3. St. Petersburg Institute for Informatics and Automation, Russian Academy of Sciences, 39 14th Line St. Petersburg, 199178, Russia

    Leonid Sheremetov

  4. Computer Science Division, University of California, 387 Soda Hall, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  5. Institute of Problems of Informatics, Academy of Sciences of Tatarstan, Mushtari st., 20 Kazan, 420012, Russia

    Ildar Batyrshin

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Batyrshin, I., Herrera-Avelar, R., Sheremetov, L., Panova, A. (2007). Moving Approximation Transform and Local Trend Associations in Time Series Data Bases. In: Batyrshin, I., Kacprzyk, J., Sheremetov, L., Zadeh, L.A. (eds) Perception-based Data Mining and Decision Making in Economics and Finance. Studies in Computational Intelligence, vol 36. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36247-0_2

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  • DOI: https://doi.org/10.1007/978-3-540-36247-0_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-36244-9

  • Online ISBN: 978-3-540-36247-0

  • eBook Packages: EngineeringEngineering (R0)

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