Application of the theory of type-2 fuzzy sets to the forecasting of categorical time series: a mathematical model and algorithm
DOI:
https://doi.org/10.31548/energiya2022.06.104Abstract
In this paper is considered the problem of forecasting categorical time series. Such series have a wide practical application in almost all spheres where judgments and expert evaluations are used. The analysis of modern research shows that the problem of taking into account the linguistic uncertainty remains insufficiently studied.
The purpose of this research is to design a time series model based on type-2 fuzzy sets theory that will allow to perform computing with words.
The type-2 fuzzy time series model gives the result in the form of a granular term, which is described by a word and a discrete interval type-2 fuzzy set.
Based on the proposed model, the fuzzy algorithm for forecasting time series has been developed, which consists of five steps: word model definition; fuzzification of time series values; fuzzy relations definition; fuzzy forecasting; defuzzification.
The high quality of the proposed forecast model is confirmed by three evaluation characteristics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE).
Key words: time series, categorical data, type-2 fuzzy set, uncertainty, computing with words, fuzzy prediction
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