Forecasting of Fuzzy Time Series Based on the Concept of the Nearest Fuzzy Sets and Tensor Models of Time Series

被引：1

作者：

Minaev, Yu. M. ^{[1
]}

Filimonova, O. Yu. ^{[2
]}

Minaeva, Yu. I. ^{[3
]}

机构：

[1] Kyiv Natl Univ Technol & Design, Kiev, Ukraine

[2] Kyiv Natl Univ Construct & Architecture, Kiev, Ukraine

[3] Taras Shevchenko Natl Univ Kyiv, Kiev, Ukraine

来源：

CYBERNETICS AND SYSTEMS ANALYSIS | 2023年 / 59卷 / 01期

关键词：

fuzzy set; tensor; missing data; singular value decomposition; F-norm;

D O I：

10.1007/s10559-023-00551-9

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：165 / 176

页数：12

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