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Fuzzy Time Series Prediction Method Based on Fuzzy Recurrent Neural Network

  • Rafik Aliev
  • Bijan Fazlollahi
  • Rashad Aliev
  • Babek Guirimov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

Abstract

One of the frequently used forecasting methods is the time series analysis. Time series analysis is based on the idea that past data can be used to predict the future data. Past data may contain imprecise and incomplete information coming from rapidly changing environment. Also the decisions made by the experts are subjective and rest on their individual competence. Therefore, it is more appropriate for the data to be presented by fuzzy numbers instead of crisp numbers. A weakness of traditional crisp time series forecasting methods is that they process only measurement based numerical information and cannot deal with the perception-based historical data represented by fuzzy numbers. Application of a fuzzy time series whose values are linguistic values, can overcome the mentioned weakness of traditional forecasting methods. In this paper we propose a fuzzy recurrent neural network (FRNN) based fuzzy time series forecasting method using genetic algorithm. The effectiveness of the proposed fuzzy time series forecasting method is tested on benchmark examples.

Keywords

Fuzzy Number Mean Absolute Percentage Error Fuzzy Neural Network Forecast Method Time Series Forecast 
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 2006

Authors and Affiliations

  • Rafik Aliev
    • 1
  • Bijan Fazlollahi
    • 2
  • Rashad Aliev
    • 3
  • Babek Guirimov
    • 1
  1. 1.Azerbaijan State Oil Academy 
  2. 2.Georgia State University 
  3. 3.Eastern Mediterranean University 

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