Interval time series forecasting can be used for forecasting special symbolic data comprising
lower and upper bounds and plays an important role in handling the complexity, instability …

The carbon trading market is an effective tool to combat greenhouse gas emissions, and as
the core issue of carbon market, carbon price can stimulate the market for technological …

Most of statistics and AI draw insights through modeling discord or variance between
sources (ie, intersource) of information. Increasingly however, research is focusing on …

In this paper, a new two-dimensional gaussian hierarchical priority fuzzy system (TGHPFS)
is proposed to handle interval-valued data. TGHPFS first performs hierarchical clustering of …

The inherent bias between inferred results from small-sized data and true values can be
mitigated through the introduction of resampling and interval regression techniques …

As typical symbolic data, interval-valued data offer a useful tool to handle massive datasets.
There has been a lot of literature focusing on researching regression models for interval …

In this paper, we propose an interval local linear method (ILLM) to fit the regression model
with interval-valued explanatory and response variables. The proposed method has no …

The literature on fixed effects panel interval-valued data models has been established.
However, less attention has been given to models that simultaneously, consider the interval …

Symbolic boxplot data can be considered as a particular case of the numerical multi-valued
variable. This kind of symbolic data is an useful exploratory tool with a simple structure for …

Interval-valued data is a complex data type which can be got by summarizing large datasets,
linear regression models for interval-valued data have been widely studied. Panel data …