Abstract
Data analysis in the context of the features of the problem domain and the dynamics of processes are significant in various industries. Uncertainty modeling based on fuzzy logic allows building approximators for solving a large class of problems. In some cases, type-2 fuzzy sets in the model are used. The article describes constructing fuzzy time series models of the analyzed processes within the context of the problem domain. An algorithm for fuzzy modeling of the time series was developed. A new time series forecasting scheme is proposed. An illustrative example of the time series modeling is presented. The benefits of contextual modeling are demonstrated.
Highlights
We propose to append the time series model with fuzzy rules for adapting the base mathematical model to the changing external conditions of the object’s functioning, which affect the changes in indicators
Fuzzify the time series tendencies à = { Ãt } = {μ(tst − tst−1 )}∀t ∈ [0, . . . , l ] and create a rule base: Rules = { RrC }, r ∈ N, where Rk is a pair ( Ãi, Ãk ), Ãi is the antecedent of the rules, Ãk is the consequent of the rules and i, k are the indices specifying the relation of the consecutive in time of the antecedent and the consequent, i < k
Correct the fuzzy sets extracted from the time series along the context boundaries of the intervals of type-2 fuzzy sets: in f ( Ãt ) = max (in f ( Ãt ), in f
Summary
The possibility of an integral representation of knowledge about the object behavior and its uncertainty is the main advantage of modeling information granules. In some research works on dynamic data analysis, time series models with information granules have been used. The works [14,21] show that time series forecasting and reducing the data dimensions can be made by information granule modeling. Large volumes of data in the databases of information systems are a great source for analysis behavior on complex organizational and technical systems Using such information for process identification is important for management [28]. The hybridization of the time series modeling approaches allows the creation of intellectual methods for data processing and analysis for decision-making systems
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