 Cybernetics and Systems Analysis /  Issue (2023, 59 (1))
Minaev Y.M.,
Filimonova O.Y.,
Minaeva Y.I.
Forecasting of fuzzy time series based on the concept of nearest fsand tensor models of time series Forecasting of fuzzy time series is considered by presenting a standard fuzzy set in the form of a tensor obtained as a result of the tensor product of components, as well as by forming a tensor sequence whose last element (the predicted fuzzy set) is calculated as an incomplete tensor (a tensor with missing elements). The singular value decomposition of the restored tensor allows us to obtain a subset of ordered pairs that is the closest (in terms of the F-norm) to the predicted fuzzy set. An example of predicting a fuzzy time series is given. © 2023, Springer Science+Business Media, LLC, part of Springer Nature. Keywords: F-norm, fuzzy set, missing data, singular value decomposition, tensor, Forecasting, Fuzzy sets, Singular value decomposition, Time series, F norms, Fuzzy-set model, Missing data, Models of time, Ordered pairs, Tensor model, Tensor products, Times series, Tensors
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Cite:
Minaev Y.M.,
Filimonova O.Y.,
Minaeva Y.I.
(2023). Forecasting of fuzzy time series based on the concept of nearest fsand tensor models of time series. Cybernetics and Systems Analysis, 59 (1), 191–204. doi: https://doi.org/10.1007/s10559-023-00551-9 http://jnas.nbuv.gov.ua/article/UJRN-0001380515 [In Ukrainian]. |
