Introduction to artificial neural systems
Introduction to artificial neural systems
Fuzzy time series and its models
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part I
Fuzzy Sets and Systems
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Computer Methods for Mathematical Computations
Computer Methods for Mathematical Computations
Fuzzy time-series based on adaptive expectation model for TAIEX forecasting
Expert Systems with Applications: An International Journal
Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations
Expert Systems with Applications: An International Journal
Adaptive-expectation based multi-attribute FTS model for forecasting TAIEX
Computers & Mathematics with Applications
A new approach based on the optimization of the length of intervals in fuzzy time series
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Using multiplicative neuron model to establish fuzzy logic relationships
Expert Systems with Applications: An International Journal
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
An efficient time series forecasting model based on fuzzy time series
Engineering Applications of Artificial Intelligence
Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization
International Journal of Approximate Reasoning
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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.