Data Science, considered as a science by itself, is in general terms, the extraction of
knowledge from data. Symbolic data analysis (SDA) gives a new way of thinking in Data …

Symbolic Data Analysis (SDA) provides a framework for the representation and analysis of
data that comprehends inherent variability. While in Data Mining and classical Statistics the …

Interval-valued time series are interval-valued data that are collected in a chronological
sequence over time. This paper introduces three approaches to forecasting interval-valued …

Structural health monitoring (SHM) has evolved over decades of continuous progress in
measuring, processing, collecting and storing massive amounts of data that can provide …

Interval time series prediction is one of the most challenging research topics in the field of
time series modeling and prediction. In view of the remarkable function approximation …

Background Dengue fever is a leading cause of severe illness and hospitalization in
Taiwan. This study sought to elucidate the linkage between dengue fever incidence and …

Histogram time series (HTS) describe situations where a distribution of values is available
for each instant of time. These situations usually arise when contemporaneous or temporal …

Modeling and forecasting symbolic data, especially interval-valued time series (ITS) data,
has received considerable attention in statistics and related fields. The core of available …

This paper introduces symbolic data analysis, explaining how it extends the classical data
models to take into account more complete and complex information. Several examples …

This paper introduces a partitioning clustering method for objects described by interval data.
It follows the dynamic clustering approach and uses and L 2 distance. Particular emphasis is …