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
A new fuzzy time-series model of fuzzy number observations
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
Time series forecasting: Obtaining long term trends with self-organizing maps
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
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
Use clustering to improve neural network in financial time series prediction
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Methodology for long-term prediction of time series
Neurocomputing
Prediction of uncertain structural responses using fuzzy time series
Computers and Structures
A FCM-based deterministic forecasting model for fuzzy time series
Computers & Mathematics with Applications
AN ENHANCED DETERMINISTIC FUZZY TIME SERIES FORECASTING MODEL
Cybernetics and Systems
Vector quantization: a weighted version for time-series forecasting
Future Generation Computer Systems
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
New model for system behavior prediction based on belief rule based systems
Information Sciences: an International Journal
A linguistic approach to time series modeling with the help of F-transform
Fuzzy Sets and Systems
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In the last decade, fuzzy time series have received more attention due their ability to deal with the vagueness and incompleteness inherent in time series data. Although various improvements, such as high-order models, have been developed to enhance the forecasting performance of fuzzy time series, their forecasting capability is mostly limited to short-term time spans and the forecasting of a single future value in one step. This paper presents a new method to overcome this shortcoming, called deterministic vector long-term forecasting (DVL). The proposed method, built on the basis of our previous deterministic forecasting method that does not require the overhead of determining the order number, as in other high-order models, utilizes a vector quantization technique to support forecasting if there are no matching historical patterns, which is usually the case with long-term forecasting. The vector forecasting method is further realized by seamlessly integrating it with the sliding window scheme. Finally, the forecasting effectiveness and stability of DVL are validated and compared by performing Monte Carlo simulations on real-world data sets.