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
A comparison of fuzzy forecasting and Markov modeling
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
Forecasting enrollments using high-order fuzzy time series and genetic algorithms: Research Articles
International Journal of Intelligent Systems
Pattern Discovery of Fuzzy Time Series for Financial Prediction
IEEE Transactions on Knowledge and Data Engineering
Fuzzy time-series based on adaptive expectation model for TAIEX forecasting
Expert Systems with Applications: An International Journal
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
Expert Systems with Applications: An International Journal
Multivariate stochastic fuzzy forecasting models
Expert Systems with Applications: An International Journal
Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model
Expert Systems with Applications: An International Journal
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
Expert Systems with Applications: An International Journal
A distance-based fuzzy time series model for exchange rates forecasting
Expert Systems with Applications: An International Journal
Temperature prediction using fuzzy time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ratio-based lengths of intervals to improve fuzzy time series forecasting
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
Time series forecasting with a hybrid clustering scheme and pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Handling forecasting problems based on two-factors high-order fuzzy time series
IEEE Transactions on Fuzzy Systems
International Journal of Wireless and Mobile Computing
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
A Critical Evaluation of Computational Methods of Forecasting Based on Fuzzy Time Series
International Journal of Decision Support System Technology
An efficient time series forecasting model based on fuzzy time series
Engineering Applications of Artificial Intelligence
Water leakage forecasting: the application of a modified fuzzy evolving algorithm
Applied Soft Computing
Relative entropy fuzzy c-means clustering
Information Sciences: an International Journal
The modeling of time series based on fuzzy information granules
Expert Systems with Applications: An International Journal
Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization
International Journal of Approximate Reasoning
Hi-index | 12.05 |
In recent years, some methods have been presented based on fuzzy time series to make predictions in many areas, such as forecasting stock price, university enrollments, the weather, etc. When using fuzzy time series for forecasting, it is obvious that the length of intervals in the universe of discourse is important due to the fact that it can affect the forecasting accuracy rate. However, most of the existing fuzzy forecasting methods based on fuzzy time series used the static length of intervals, i.e., the same length of intervals. The drawback of the static length of intervals is that the historical data are roughly put into the intervals, even if the variance of the historical data is not high. Moreover, the forecasting accuracy rates of the existing fuzzy forecasting methods are not good enough. Therefore, we must develop a new fuzzy forecasting method to overcome the drawbacks of the existing fuzzy forecasting methods to increase the forecasting accuracy rates. In this paper, we propose a multivariate fuzzy forecasting method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time series and automatic clustering techniques to overcome the drawbacks of the existing methods. First, we propose a new automatic clustering algorithm to generate different lengths of intervals in the universe of discourse. Then, we propose a new multivariate fuzzy forecasting method to forecast the TAIEX based on fuzzy time series and the proposed automatic clustering algorithm. The proposed multivariate fuzzy forecasting method gets higher average forecasting accuracy rates than the existing methods for forecasting the TAIEX.