The fuzzy time series model uses a four-step framework to make forecast: (1) define the universe of discourse and partition it into intervals; (2) determine the fuzzy sets on the universe of discourse and fuzzify the time series; (3) build the model of the existing fuzzy logic relationships in the fuzzified time series ...
Jan 13, 2020
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Jul 23, 2022 · As cited by (Chen, 1996), fuzzy time series is a new concept in statistical modeling process which can be used to deal with forecasting ...
Unlike traditional time series that deal with real numbers, the fuzzy set based time series F(t) is a collection of time-indexed fuzzy linguistic terms f1(t),f2 ...
This paper summarizes and reviews past twenty five year's contribution in the area of Fuzzy time series forecasting.
In this paper the framework (algorithm) of multiple time series forecasting, based on fuzzy techniques, is pro- posed. We applied the traditional decomposition ...
The aim of this contribution is to show the opportunities of applying of fuzzy time series models to predict multiple heterogeneous time series, ...
The purpose of this paper is to present the results of a systematic literature review regarding the development of fuzzy-based models for time series ...
The aim of this contribution is to show the opportunities of applying of fuzzy time series models to predict multiple heterogeneous time series, ...
As a consequence of employing these techniques, data utilization is improved by: (1) minimizing the loss of forecast rules; (2) minimizing the number of pattern.
This paper is devoted to a method for the forecasting of seasonal time series. The core of our approach is based on the fuzzy transform and fuzzy natural ...