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Chaotic Oscillations in Real Economic Time Series Data: Evaluation of Logistic Model Fit and Forecasting Performance

  • John Dimoticalis
  • Sotiris Zontos
  • Christos H. Skiadas
Part of the Applied Optimization book series (APOP, volume 19)

Abstract

In this article, we investigate the existence of chaotic oscillations in real economic time series data. To achieve this, we use the well known logistic model equation in discrete form. We apply the logistic model to the M3 Competition time series data. At first, the data transformed in order to vary in the close interval [0,1]. Then, we search for those of the parameter values of the logistic equation that best fit them. By this approach we find a number of time series which best fitted by logistic model for values of control parameter b>3.57 (the chaotic limit). Thus, the data evolution seems to follow chaotic paths. Some implications are presented, especially the expected sensitive dependence on initial conditions (model parameter values) for chaotic paths. This issue is investigated further performing an evaluation of Logistic model forecasting performance. Finally some concluding remarks and future directions are presented.

Keywords

Logistic Model M3 Competition Forecasting Time Series Analysis Chaos 

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Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • John Dimoticalis
    • 1
  • Sotiris Zontos
    • 1
  • Christos H. Skiadas
    • 1
  1. 1.Dept. of Production Engineering and ManagementTechnical University of CreteGreece

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