The least-squares technique has been shown to possess valuable properties as a method of
the parameter estimation of classic and fuzzy regression analysis. However, the behavior …

It is worth to note that estimated parameters of any fuzzy regression model as well as its
goodness-of-fit value depend on the objective function being optimized. Thus, it is not easy …

In this paper, an exponential autoregressive model for complex time series data is
presented. As for estimating the parameters of this nonlinear model, a three-step procedure …

Pattern recognition and data mining using support vector machine (SVM) have been the
focus of widespread researches in recent decades. In SVM, a hyper-plane is designed to …

This paper is an attempt to develop a novel linear regression model with autocorrelated
fuzzy error terms and exact predictors and fuzzy responses. The conventional Durbin …

The least squares method is a frequently used technique in fuzzy regression analysis.
However, it is highly sensitive to outliers in the dataset. To address this challenge, we …

The conventional fuzzy least-squares time series models show undesirable performance
when the fuzzy data set involves the outliers. By introducing a strategy to detect the outliers …

In this paper, a nonlinear time series model is developed for the case when the underlying
time series data are reported by LR fuzzy numbers. To this end, we present a three-stage …

The time series analysis is mainly aimed at establishing a fuzzy prediction model based on a
set of real-valued time series data. To achieve this goal, the present paper proposes a …

Parametric time series models typically consists of model identification, parameter
estimation, model diagnostic checking, and forecasting. However compared with parametric …