Time series pattern mining (TSPM) finds correlations or dependencies in same series or in multiple time series. When the numerous instances of multiple time series data are associ...
Abstract--Imbalanced data sets present a particular challenge to the data mining community. Often, it is the rare event that is of interest and the cost of misclassifying the rare ...
Time series are found widely in engineering and science. We study multiagent forecasting in time series, drawing from literature on time series, graphical models, and multiagent s...
In this paper, we propose a new diagnostic checking tool for fuzzy rule-based modelling of time series. Through the study of the residuals in the Lagrange Multiplier testing framew...
Abstract. We consider the class of applications that manage time series (TS) and propose a data model and a query language that let these applications manipulate TS data sets at a ...
Temporal causal modeling has been a highly active research area in the last few decades. Temporal or time series data arises in a wide array of application domains ranging from med...
There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to index and mine very large collections of time series. Examples...
Alessandro Camerra, Themis Palpanas, Jin Shieh, Ea...
—This paper provides a framework for generating high resolution time sequences of 3D images that show the dynamics of cerebral blood flow. These sequences have the potential to ...
Andrew Copeland, Rami Mangoubi, Mukund N. Desai, S...
One of the most important challenges for the researchers in the 21st Century is related to global heating and climate change that can have as consequence the intensification of na...
Luciana A. S. Romani, Ana Maria Heuminski de &Aacu...
Common measures of term importance in information retrieval (IR) rely on counts of term frequency; rare terms receive higher weight in document ranking than common terms receive. ...