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Fuzzy Control pp 331-345 | Cite as

Fuzzy Time Series Analysis

  • Steffen F. Bocklisch
  • Michael Päßler
Part of the Advances in Soft Computing book series (AINSC, volume 6)

Abstract

A modeling method is suggested in this paper, which permits building multidimensional fuzzy models of time series consisting of fuzzy prototypes. These models have to be trained in a so-called period of learning and are suitable for short, medium and long range forecasts. The prediction of an incomplete time series is based on fuzzy classification to the prototypes. The results are grades of membership. In principle, these grades and further courses of prototypes are used to forecast the time series.

Keywords

Time Series Feature Vector Traffic Flow Fuzzy Model Trigger Point 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Steffen F. Bocklisch
    • 1
  • Michael Päßler
    • 1
  1. 1.TU ChemnitzChemnitzGermany

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