A New Approach Based on Artificial Neural Networks for High Order Bivariate Fuzzy Time Series

Egrioglu E., Uslu V. R., Yolcu U., Basaran M. A., Hakan A. C.

13th World Conference on Soft Computing in Industrial Application, ELECTR NETWORK, 01 Ocak 2008, cilt.58, ss.265-267 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 58
  • Doi Numarası: 10.1007/978-3-540-89619-7_26
  • Basıldığı Ülke: ELECTR NETWORK
  • Sayfa Sayıları: ss.265-267
  • Marmara Üniversitesi Adresli: Hayır

Özet

When observations of time series are defined linguistically or do not follow the assumptions required for time series theory, the classical methods of time series analysis do not cope with fuzzy numbers and assumption violations. Therefore, forecasts are not reliable. [8], [9] gave a definition of fuzzy time series which have fuzzy observations and proposed a forecast method for it. In recent years, many researches about univariate fuzzy time series have been conducted. In [6], [5], [7], [4] and [10] bivariate fuzzy time series approaches have been proposed. In this study, a new method for high order bivariate fuzzy time series in which fuzzy relationships are determined by artificial neural networks (ANN) is proposed and the real data application of the proposed method is presented.