Fuzzy time series and its models
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part I
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part II
Fuzzy Sets and Systems
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Handling forecasting problems using fuzzy time series
Fuzzy Sets and Systems
Data mining: concepts and techniques
Data mining: concepts and techniques
Case Generation Using Rough Sets with Fuzzy Representation
IEEE Transactions on Knowledge and Data Engineering
Forecasting enrollments using high-order fuzzy time series and genetic algorithms: Research Articles
International Journal of Intelligent Systems
Multi-attribute fuzzy time series method based on fuzzy clustering
Expert Systems with Applications: An International Journal
A bivariate fuzzy time series model to forecast the TAIEX
Expert Systems with Applications: An International Journal
The adaptive neuro-fuzzy model for forecasting the domestic debt
Knowledge-Based Systems
Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A computational method of forecasting based on high-order fuzzy time series
Expert Systems with Applications: An International Journal
Similarity relations and fuzzy orderings
Information Sciences: an International Journal
A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting
Information Sciences: an International Journal
Cardinality-based fuzzy time series for forecasting enrollments
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
An improved fuzzy forecasting method for seasonal time series
Expert Systems with Applications: An International Journal
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
Forecasting tourism demand based on improved fuzzy time series model
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
Handling forecasting problems based on high-order fuzzy logical relationships
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A generalized method for forecasting based on fuzzy time series
Expert Systems with Applications: An International Journal
Fuzzy based trend mapping and forecasting for time series data
Expert Systems with Applications: An International Journal
Temperature prediction using fuzzy time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Multivariate Heuristic Model for Fuzzy Time-Series Forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
Pattern classification using neural networks
IEEE Communications Magazine
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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In this article, we present a new model based on hybridization of fuzzy time series theory with artificial neural network (ANN). In fuzzy time series models, lengths of intervals always affect the results of forecasting. So, for creating the effective lengths of intervals of the historical time series data set, a new ''Re-Partitioning Discretization (RPD)'' approach is introduced in the proposed model. Many researchers suggest that high-order fuzzy relationships improve the forecasting accuracy of the models. Therefore, in this study, we use the high-order fuzzy relationships in order to obtain more accurate forecasting results. Most of the fuzzy time series models use the current state's fuzzified values to obtain the forecasting results. The utilization of current state's fuzzified values (right hand side fuzzy relations) for prediction degrades the predictive skill of the fuzzy time series models, because predicted values lie within the sample. Therefore, for advance forecasting of time series, previous state's fuzzified values (left hand side of fuzzy relations) are employed in the proposed model. To defuzzify these fuzzified time series values, an ANN based architecture is developed, and incorporated in the proposed model. The daily temperature data set of Taipei, China is used to evaluate the performance of the model. The proposed model is also validated by forecasting the stock exchange price in advance. The performance of the model is evaluated with various statistical parameters, which signify the efficiency of the model.