Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Induction of fuzzy rules and membership functions from training examples
Fuzzy Sets and Systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Fuzzy regression methods—a comparative assessment
Fuzzy Sets and Systems
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Dynamic Programming
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Automatic tuning of complex fuzzy systems with Xfuzzy
Fuzzy Sets and Systems
Advances in Fuzzy Clustering and its Applications
Advances in Fuzzy Clustering and its Applications
Methodology for long-term prediction of time series
Neurocomputing
Finding the embedding dimension and variable dependencies in time series
Neural Computation
Non-parametric residual variance estimation in supervised learning
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
The WM method completed: a flexible fuzzy system approach to data mining
IEEE Transactions on Fuzzy Systems
Fuzzy rule-based networks for control
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
A linguistic approach to time series modeling with the help of F-transform
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
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We propose an automatic methodology framework for short- and long-term prediction of time series by means of fuzzy inference systems. In this methodology, fuzzy techniques and statistical techniques for nonparametric residual variance estimation are combined in order to build autoregressive predictive models implemented as fuzzy inference systems. Nonparametric residual variance estimation plays a key role in driving the identification and learning procedures. Concrete criteria and procedures within the proposed methodology framework are applied to a number of time series prediction problems. The learn from examples method introduced by Wang and Mendel (W&M) is used for identification. The Levenberg-Marquardt (L-M) optimization method is then applied for tuning. The W&M method produces compact and potentially accurate inference systems when applied after a proper variable selection stage. The L-M method yields the best compromise between accuracy and interpretability of results, among a set of alternatives. Delta test based residual variance estimations are used in order to select the best subset of inputs to the fuzzy inference systems as well as the number of linguistic labels for the inputs. Experiments on a diverse set of time series prediction benchmarks are compared against least-squares support vector machines (LS-SVM), optimally pruned extreme learning machine (OP-ELM), and k-NN based autoregressors. The advantages of the proposed methodology are shown in terms of linguistic interpretability, generalization capability and computational cost. Furthermore, fuzzy models are shown to be consistently more accurate for prediction in the case of time series coming from real-world applications.