Recently, there seems to be increased interest in time series forecasting using soft
computing (SC) techniques, such as fuzzy sets, artificial neural networks (ANNs), rough set …

The present research proposes a novel probabilistic intuitionistic fuzzy time series
forecasting (PIFTSF) model using support vector machine (SVM) to address both uncertainty …

Financial time series prediction problems, for decision-makers, are always crucial as they
have a wide range of applications in the public and private sectors. This study presents a …

In this study, a new Fuzzy Time Series (FTS) model based on the Fuzzy K-Medoid (FKM)
clustering algorithm is proposed in order to forecast air pollution. FTS models generally have …

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have
been generally preferred for determination of fuzzy logic relationships. The reason of this is …

Big data mining, analysis and forecasting always play a vital role in modern economic and
industrial fields, and selecting an optimization model to improve time series' forecasting …

Fuzzy time series methods, which do not require the strict assumptions of classical time
series methods, generally consist of three stages as fuzzification of crisp time series …

Within classic time series approaches, a time series model can be studied under 3 groups,
namely AR (autoregressive model), MA (moving averages model) and ARMA …

In the past few years, non-stochastic fuzzy time series (FTS) models have drawn remarkable
attention of researchers from different domains. Unlike traditional stochastic models, FTS …

In case of outlier (s) it is inevitable that the performance of the fuzzy time series prediction
methods is influenced adversely. Therefore, current prediction methods will not be able to …