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 …

Time series analysis and forecasting future values has been a major research focus since
years ago. Time series analysis and forecasting in time series data finds it significance in …

Time Series is an orderly sequence of values of a variable in a particular domain.
Forecasting is a challenging task in the area of Time Series Analysis. Forecasting has a …

In this paper, we propose a new fuzzy time series (FTS) forecasting method based on the
proportions of intervals and particle swarm optimization (PSO) techniques. First, it uses PSO …

In this study, a fuzzy time series-Markov chain approach for analyzing the linguistic or a
small sample time series data is proposed to further enhance the predictive accuracy. By …

Time-series models have been used to make predictions in the areas of stock price
forecasting, academic enrollment and weather, etc. However, in stock markets, reasonable …

In fuzzy time series forecasting various methods have been developed to establish the fuzzy
relations on time series data having linguistic values for forecasting the future values …

В связи с ростом хранимых, упорядоченных во времени, данных о характеристиках
объектов, процессов и систем в промышленности, экономике, медицине, образовании …

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 …

This research introduces a neutrosophic forecasting approach based on neutrosophic time
series (NTS). Historical data can be transformed into neutrosophic time series data to …