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
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
A fuzzy controller with evolving structure
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Bio-inspired systems (BIS)
The use of fuzzy set theory for forecasting corporate tax revenues
Fuzzy Sets and Systems
Newspaper demand prediction and replacement model based on fuzzy clustering and rules
Information Sciences: an International Journal
Evolving fuzzy classifiers using different model architectures
Fuzzy Sets and Systems
Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques
Information Sciences: an International Journal
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
Applied Soft Computing
Multivariate fuzzy forecasting based on fuzzy time series and automatic clustering techniques
Expert Systems with Applications: An International Journal
Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications
Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models
IEEE Transactions on Fuzzy Systems
SaFIN: A Self-Adaptive Fuzzy Inference Network
IEEE Transactions on Neural Networks - Part 1
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This paper investigates the use of evolving fuzzy algorithms in forecasting. An evolving Takagi-Sugeno (eTS) algorithm, which is based on a recursive version of the subtractive algorithm is considered. It groups data into several clusters based on Euclidean distance between the relevant independent variables. The Mod eTS algorithm, which incorporates a modified dynamic update of cluster radii while accommodating new available data is proposed. The created clusters serve as a base for fuzzy If-Then rules with Gaussian membership functions which are defined using the cluster centres and have linear functions in the consequent i.e., Then parts of rules. The parameters of the linear functions are calculated using a weighted version of the Recursive Least Squares algorithm. The proposed algorithm is applied to a leakage forecasting problem faced by one of the leading UK water supplying companies. Using the real world data provided by the company the forecasting results obtained from the proposed modified eTS algorithm, Mod eTS, are compared to the standard eTS algorithm, exTS, eTS+ and fuzzy C-means clustering algorithm and some standard statistical forecasting methods. Different measures of forecasting accuracy are used. The results show higher accuracy achieved by applying the algorithm proposed compared to other fuzzy clustering algorithms and statistical methods. Similar results are obtained when comparing with other fuzzy evolving algorithms with dynamic cluster radii. Furthermore the algorithm generates typically a smaller number of clusters than standard fuzzy forecasting methods which leads to more transparent forecasting models.