Interval type-2 fuzzy set based time series forecasting using a data-driven partitioning approach

Pinto, Arthur Caio Vargas e; Fernandes, Thiago E.; Silva, Petrônio Cândido de Lima e; Guimarães, Frederico Gadelha; Wagner, Christian; Aguiar, Eduardo Pestana de

doi:10.1007/s12530-022-09452-2

Cited by 6 publications

(1 citation statement)

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“…In addition to the mentioned fuzzy sets, which are more common, some references developed different FTS methods applying other types of fuzzy sets, including picture fuzzy sets and picture fuzzy clustering [86], Type-2 fuzzy sets [87][88][89][90], hesitant differential fuzzy sets [91], non-stationary fuzzy sets [11,92] and so on.…”

Section: The Membership Functionmentioning

confidence: 99%

A Tutorial on Fuzzy Time Series Forecasting Models: Recent Advances and Challenges

Lucas¹,

Orang²,

Silva³

et al. 2022

Learn. Nonlin. Mod.

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Time series forecasting is a powerful tool in planning and decision making, from traditional statistical models to soft computing and artificial intelligence approaches several methods have been developed to generate increasingly accurate forecasts. Fuzzy Time Series (FTS) methods have been introduced in the early 1990’s to handle data uncertainty and to undercome the statistical assumptions of linearity. Many studies have been reporting their good accuracy, simplicity, potential for interpretability and reduced computational complexity. This paper presents a tutorial for FTS methods. First, a review of the relevant literature is made, offering a foundation on the main concepts and FTS-based models for different time series and different types of forecasts. Then, the current challenges and possible solutions, are discussed alongside a timeline of the research developed in this area by the authors that aims at filling some of these gaps. Finally, a tutorial on the pyFTS library is presented. PyFTS is an open and free library coded in Python programming language that was developed by the MINDS Lab (Laboratory of Machine Intelligence and Data Science) and, also provides a set of transformation functions for pre-processing time series and a set of metrics and databases for benchmarking, in addition to implementing several FTS models in the literature.

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