Fuzzy regression model is an alternative to evaluate the relation between independent
variables and dependent variable among the forecasting models when the data are not …

Several time-variant fuzzy time series models have been developed during the last decade.
These models usually focus on forecasting stationary of trend time series, but they are not …

In the literature, the most studied of fuzzy time series for the purpose of forecasting is the first
order fuzzy time series model. In this model, only the first lagged variable is used when …

Based on the seasonal time series ARIMA (p, d, q)(P, D, Q) s model (SARIMA) and fuzzy
regression model, we combine the advantages of two methods to propose a procedure of …

When fuzzy time series include a seasonal component, conventional fuzzy time series
models are not sufficient. For such fuzzy time series, lagged variables which are around the …

This paper proposes a fuzzy seasonal ARIMA (FSARIMA) forecasting model, which
combines the advantages of the seasonal time series ARIMA (SARIMA) model and the fuzzy …

Fuzzy time series approach has been widely used to analyze real-world time series in recent
years since using this approach has some important advantages. Various fuzzy time series …

Forecasting for time series has always been of interest to statisticians and data scientists
because it offers a lot of benefits in reality. This study proposes the fuzzy time series model …

In this study, we develop a novel moving average forecasting approach based on fuzzy time
series data set. The main objective of applying this moving average approach in develop …

A number of fuzzy time series models have been designed and developed during the last
decade. One problem of these models is that they only provide a single-point forecasted …