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A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks

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  • Aladag, Cagdas Hakan
  • Yolcu, Ufuk
  • Egrioglu, Erol

Abstract

Many fuzzy time series approaches have been proposed in recent years. These methods include three main phases such as fuzzification, defining fuzzy relationships and, defuzzification. Aladag et al. [2] improved the forecasting accuracy by utilizing feed forward neural networks to determine fuzzy relationships in high order fuzzy time series. Another study for increasing forecasting accuracy was made by Cheng et al. [6]. In their study, they employ adaptive expectation model to adopt forecasts obtained from first order fuzzy time series forecasting model. In this study, we propose a novel high order fuzzy time series method in order to obtain more accurate forecasts. In the proposed method, fuzzy relationships are defined by feed forward neural networks and adaptive expectation model is used for adjusting forecasted values. Unlike the papers of Cheng et al. [6] and Liu et al. [14], forecast adjusting is done by using constraint optimization for weighted parameter. The proposed method is applied to the enrollments of the University of Alabama and the obtained forecasting results compared to those obtained from other approaches are available in the literature. As a result of comparison, it is clearly seen that the proposed method significantly increases the forecasting accuracy.

Suggested Citation

  • Aladag, Cagdas Hakan & Yolcu, Ufuk & Egrioglu, Erol, 2010. "A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(4), pages 875-882.
    • Handle: RePEc:eee:matcom:v:81:y:2010:i:4:p:875-882
    DOI: 10.1016/j.matcom.2010.09.011
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    References listed on IDEAS

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    1. Huarng, Kunhuang & Yu, Tiffany Hui-Kuang, 2006. "The application of neural networks to forecast fuzzy time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(2), pages 481-491.
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    1. Kaur, Gurbinder & Dhar, Joydip & Guha, Rangan Kumar, 2016. "Minimal variability OWA operator combining ANFIS and fuzzy c-means for forecasting BSE index," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 122(C), pages 69-80.
    2. Sushree Subhaprada Pradhan & Sibarama Panigrahi, 2024. "A study and development of high‐order fuzzy time series forecasting methods for air quality index forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2635-2658, November.

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