On the relationship between neural networks, pattern recognition and intelligence
International Journal of Approximate Reasoning - Special issue on fuzzy logic and neural networks for pattern recognition and control
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Handbook of Granular Computing
Handbook of Granular Computing
Computational Intelligence: A Compendium
Computational Intelligence: A Compendium
Fuzzy Systems Engineering: Toward Human-Centric Computing
Fuzzy Systems Engineering: Toward Human-Centric Computing
Human-Centric Information Processing Through Granular Modelling
Human-Centric Information Processing Through Granular Modelling
Interpreting concept learning in cognitive informatics and granular computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Fuzzy Sets and Systems
A review on time series data mining
Engineering Applications of Artificial Intelligence
Artificial Intelligence in Medicine
Joint segmentation and classification of time series using class-specific features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Toward a Theory of Granular Computing for Human-Centered Information Processing
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
Iterative meta-clustering through granular hierarchy of supermarket customers and products
Information Sciences: an International Journal
The modeling of time series based on fuzzy information granules
Expert Systems with Applications: An International Journal
Hierarchical description of uncertain information
Information Sciences: an International Journal
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In spite of the evident diversity of models of signals and time series, there is still an urgent need to develop constructs that are both accurate and highly interpretable (human-centric). While a great deal of research has been devoted to the design of nonlinear models of time series (with anticipation of achieving high accuracy of prediction), an issue of interpretability (transparency) of the models remains an evident and ongoing challenge. The user-friendliness of models of time series comes hand in hand with an ability of humans to perceive and process abstract entities rather than plain numeric entities. With this regard, information granules and Granular Computing play an essential role. The use of information granules gives rise to a concept of granular models of time series or granular models of signals and time series, in brief. A granular interpretation of temporal data, where the role of information granularity is of paramount interest and effectively supports a human-centric description of relationships existing within data. This study revisits generic concepts of information granules and Granular Computing in this setting and elaborates on a fundamental way of forming information granules (both sets - intervals as well as fuzzy sets) through applying a principle of justifiable granularity. The granular representation of time series is then discussed with a number of representation alternatives. A question of forming adjustable temporal slices (time windows) using which information granules are formed is discussed. With this regard presented is an optimization criterion of a sum of volumes of information granules whose minimization is realized through some methods of evolutionary or population-based optimization techniques. A series of illustrative examples is also discussed.