Statistical regression analysis is a powerful and reliable method to determine the impact of
one or several independent variable (s) on a dependent variable. It is the most widely used …

Most of previous studies on fuzzy regression analysis have a common characteristic of
increasing spreads for the estimated fuzzy responses as the independent variable increases …

To estimate the parameters of fuzzy linear regression models with fuzzy output and crisp
inputs, we develop a mathematical programming model in this paper. The method is …

To model fuzzy binary observations, a new model named “Fuzzy Logistic Regression” is
proposed and discussed in this study. In fact, due to the vague nature of binary observations …

This research aims to be guided decision-makers in future planning by estimating the
tendency of data consistently. In this context, it is thought that the integration of the …

Due to missing data or a short sensing period, only a minimal amount of data can be
captured in some IoT cases. Making accurate time series predictions with tiny samples is a …

Using publicly available information effectively is important to remain competitive in
technology related industries. The main difficulty in this is determining how to use the …

In time series analysis, such as other statistical problems, we may confront imprecise
quantity. One case is a situation in which the observations related to underlying systems are …

Abstract The grey model GM (1, 1) is a popular forecasting method when using limited time
series data and is successfully applied to management and engineering applications. On …

In this study, an adaptive network‐based fuzzy inference system (ANFIS) and a neural
network were tested for the ability of these techniques to forecast the annual excess returns …