The financial market volatility has been a focus of study for experts over past decades. While
stockbrokers and investors expect reliable projections of future stock indices, it instead …

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

Over the last three decades, several researchers have been putting their efforts into
developing non-deterministic fuzzy time series (FTS) models using the traditional fuzzy set …

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 …

Over the years, numerous fuzzy time-series forecasting (FTSF) models have been
developed to handle the uncertainty and non-determinism in the time-series (TS) data. To …

Numerous fuzzy time series (FTS) predictive models had been envisaged in past decades to
cope with complicated and undetermined circumstances. The key elements: namely …

Pi‐sigma artificial neural networks have very good performance for forecasting problems
because of their highly nonlinear model structure. Some time series can be forecasted better …

Artificial neural network models have been frequently used in time series forecasting
problems as an alternative to many classical forecasting models. Although multi-layer …

In the present scenario, fuzzy time series forecasting (FTSF) is an interesting concept by the
researchers to approach the uncertainty in the dataset. In the current study, we proposed a …

Fuzzy Time Series Forecasting (TSF) is an approach for dealing with uncertainty in time
series data that uses fuzzy logic. The Hesitant Fuzzy Set (HFS) theory better emphasizes the …