Advertisement

Event Detection in Financial Time Series by Immune-Based Approach

  • Tomasz Pelech
  • Jan T. Duda
Part of the Advances in Soft Computing book series (AINSC, volume 35)

Abstract

The paper presents a concept of immune paradigm application to monitoring of company environment. Short-time prediction of stock rates is used as a basic tool to vigil relevant events, viewed as switching between “healthy” and “ill” behavior of the monitored quotations. Two predictive formulas are applied alternatively to recognize the behavior kind. “Illness” detection rules are proposed, based on the prediction efficiency evaluated in moving windows. Parameters of the predictors are modified according to the immune paradigm.

Keywords

Event Detection Financial Time Series Daily Increment Immune Memory Detection Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    1. Asokan M.V., Chenouri S. et al. (2001) ARCH and GARCH Models. University of WaterlooGoogle Scholar
  2. 2.
    2. Augustynek A., Duda J.T. et al. (2003) Matematyczne prognozowanie cen miedzina gieldzie LME. Kwartalnik Automatyka, 4, AGH-UST Univ.Press, KrakowGoogle Scholar
  3. 3.
    3. Box G.E.P., Jenkins G.M. (1976) Time Series Analysis. Holden-Day, San FranciscozbMATHGoogle Scholar
  4. 4.
    4. Duda J.T., Augustynek A. (2004) On Possibilities of Improvement of Short-Term Predictions of Stock Indices with Regression Models. [In:] Company Management in Conditions of European Integration, Part 2. AGH-UST Univ. Press, KrakowGoogle Scholar
  5. 5.
    5. Hofmeyr S.A., Forrest S. (1999) Immunity by Design: An Artificial Immune System. Proc. of Genetic and Evolutionary Computation Conf., San FranciscoGoogle Scholar
  6. 6.
    6. Holt C.C. (1957) Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages. Carnegie Institute Of Technology. Pittsburgh, PennsylvaniaGoogle Scholar
  7. 7.
    7. Manczak K., Nachorski M. (1981) Komputerowa identy.kacja obiektow dynamicznych. PWN, WarsawGoogle Scholar
  8. 8.
    8. Pelech T., Duda J.T. (2005) Application of Immune Paradigm to Monitoring of Stock Market Indices. [In:] Problems of Mechanical Engineering and Robotics. AGH-UST Univ.Press, KrakowGoogle Scholar
  9. 9.
    9. Pelech T., Duda J.T. (2005) Immune Algorithm of Stock Rates Parallel Monitoring. [In:] Information Systems and Computational Methods in Management. AGH-UST Univ. Press, KrakowGoogle Scholar
  10. 10.
    10. Timmis J., Knight T. et al. (2004) An Overview of Artificial Immune Systems. [In:] Computation in Cells and Tissues: Perspectives and Tools for Thought. Springer.Google Scholar
  11. 11.
    11. Wierzchon S.T. (s2001) Sztuczne systemy immunologiczne. Teoria i zastosowania. Wyd.Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Tomasz Pelech
    • 1
  • Jan T. Duda
    • 1
  1. 1.AGH-University of Science and TechnologyKrakówPoland

Personalised recommendations