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
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
On Similarity-Based Queries for Time Series Data
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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
Expert Systems with Applications: An International Journal
Deterministic fuzzy time series model for forecasting enrollments
Computers & Mathematics with Applications
A bivariate fuzzy time series model to forecast the TAIEX
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
Handling forecasting problems based on two-factors high-order fuzzy time series
IEEE Transactions on Fuzzy Systems
Adaptive-expectation based multi-attribute FTS model for forecasting TAIEX
Computers & Mathematics with Applications
Deterministic vector long-term forecasting for fuzzy time series
Fuzzy Sets and Systems
Adaptive time-variant models for fuzzy-time-series forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Time series labeling algorithms based on the K-nearest neighbors' frequencies
Expert Systems with Applications: An International Journal
Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering
Expert Systems with Applications: An International Journal
A new time-invariant fuzzy time series forecasting method based on genetic algorithm
Advances in Fuzzy Systems - Special issue on Fuzzy Function, Relations, and Fuzzy Transforms (2012)
Expert Systems with Applications: An International Journal
Determination of temporal information granules to improve forecasting in fuzzy time series
Expert Systems with Applications: An International Journal
Advances in Fuzzy Systems
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The study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling uncertainty and vagueness inherent in the data collected. A variety of forecasting models including high-order models have been devoted to improving forecasting accuracy. However, the high-order forecasting approach is accompanied by the crucial problem of determining an appropriate order number. Consequently, such a deficiency was recently solved by Li and Cheng [S.-T. Li, Y.-C. Cheng, Deterministic Fuzzy time series model for forecasting enrollments, Computers and Mathematics with Applications 53 (2007) 1904-1920] using a deterministic forecasting method. In this paper, we propose a novel forecasting model to enhance forecasting functionality and allow processing of two-factor forecasting problems. In addition, this model applies fuzzy c-means (FCM) clustering to deal with interval partitioning, which takes the nature of data points into account and produces unequal-sized intervals. Furthermore, in order to cope with the randomness of initially assigned membership degrees of FCM clustering, Monte Carlo simulations are used to justify the reliability of the proposed model. The superior accuracy of the proposed model is demonstrated by experiments comparing it to other existing models using real-world empirical data.