Fuzzy time series and its models
Fuzzy Sets and Systems
Integrating and accelerating tabu search, simulated annealing, and genetic algorithms
Annals of Operations Research - Special issue on Tabu search
Forecasting enrollments with fuzzy time series—part I
Fuzzy Sets and Systems
Some properties of defuzzification neural networks
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part II
Fuzzy Sets and Systems
A comparison of fuzzy forecasting and Markov modeling
Fuzzy Sets and Systems
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
Expert Systems with Applications: An International Journal
Fuzzy relation analysis in fuzzy time series model
Computers & Mathematics with Applications
A note on fuzzy time-series model
Fuzzy Sets and Systems
Ratio-based lengths of intervals to improve fuzzy time series forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multi-agent solution for reduction of bullwhip effect in fuzzy supply chains
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 12.05 |
In this paper, a new fuzzy time series based on high-order fuzzy logical relationships and Tabu Search is presented. The proposed method constructs N-factor high-order fuzzy logical relationships based on the historical data and uses Tabu Search and a parametric fuzzy inference system to adjust the length of intervals in the universe of discourse for prediction to increase the forecasting accuracy rate. We have applied our model for different cases with different factors. The model is applied for prediction of auto industry production of Iranian companies with a three-factor fuzzy time series model. The results show that the proposed method gets a higher forecasting accuracy rate than the existing methods in all cases.