Ensemble-based methods are among the most widely used techniques for data stream
classification. Their popularity is attributable to their good performance in comparison to …

In data stream environments, drift detection methods are used to identify when the context
has changed. This paper evaluates eight different concept drift detectors (ddm, eddm, pht …

Electricity load forecasting has been attracting research and industry attention because of its
importance for energy management, infrastructure planning, and budgeting. In recent years …

Recent trends in the Machine Learning (ML) and in particular Deep Learning (DL) domains
have demonstrated that with the availability of massive amounts of time series, ML and DL …

A hands-on approach to tasks and techniques in data stream mining and real-time analytics,
with examples in MOA, a popular freely available open-source software framework. Today …

Abstract Massive Online Analysis (MOA) is a software environment for implementing
algorithms and running experiments for online learning from evolving data streams. MOA is …

Most streaming decision models evolve continuously over time, run in resource-aware
environments, and detect and react to changes in the environment generating data. One …

Micro-blogs are a challenging new source of information for data mining techniques. Twitter
is a micro-blogging service built to discover what is happening at any moment in time …

The dramatic rise in the use of social network platforms such as Facebook or Twitter has
resulted in the availability of vast and growing user-contributed repositories of data …

The problem of real-time extraction of meaningful patterns from time-changing data streams
is of increasing importance for the machine learning and data mining communities …