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
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
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
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
Forecasting time series with a new architecture for polynomial artificial neural network
Applied Soft Computing
Time series prediction with single multiplicative neuron model
Applied Soft Computing
A neural clustering and classification system for sales forecasting of new apparel items
Applied Soft Computing
Use clustering to improve neural network in financial time series prediction
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Perception-based approach to time series data mining
Applied Soft Computing
A new approach for determining the length of intervals for fuzzy time series
Applied Soft Computing
AN ENHANCED DETERMINISTIC FUZZY TIME SERIES FORECASTING MODEL
Cybernetics and 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
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The emergence of fuzzy time series has recently received more attention because of its capability of dealing with vagueness and incompleteness inherent in data. Deriving an effective and useful forecasting model has been a challenge task. In the previous work, the authors addressed two crucial issues, namely controlling uncertainty and effectively partitioning intervals, as well as developed a deterministic forecasting model to manage these issues. However, their model neglected the distribution and uncertainty of data points and can only provide scalar forecasting, thus limiting its usefulness. This study expands the deterministic forecasting model to improve forecasting capability. We propose a vector forecasting model that allows the prediction of a vector of future values in one step, by integrating the technologies of sliding window and fuzzy c-means clustering, to deal with vector forecasting and interval partitioning. Experimental results and analysis using Monte Carlo simulations for two experiments, both with three data sets, validate the effectiveness of the proposed forecasting model.