One of the most significant findings of traffic measurement studies over the last decade has
been the observed self-similarity in packet network traffic. Subsequent research has focused …

Despite the apparent randomness of the Internet, we discover some surprisingly simple
power-laws of the Internet topology. These power-laws hold for three snapshots of the …

This comprehensive text gives an interesting and useful blend of the mathematical,
probabilistic and statistical tools used in heavy-tail analysis. It is uniquely devoted to heavy …

With the growing importance of time series clustering research, particularly for similarity
searches amongst long time series such as those arising in medicine or finance, it is critical …

Temporal data mining deals with the harvesting of useful information from temporal data.
New initiatives in health care and business organizations have increased the importance of …

The explosion of the World Wide Web as a medium for information dissemination has made
it important to understand its characteristics, in particular the distribution of its file sizes. This …

Abstract Machine learning has seen a significant surge and uptake across many diverse
applications. The high flexibility, adaptability, and computing capabilities it provides extend …

Accurately characterizing end-to-end Internet dynamics is exceptionally difficult, due to the
network's immense heterogeneity. It can be impossible to gauge the generality of findings …

In apparent contrast to the well-documented self-similar (ie, monofractal) scaling behavior of
measured LAN traffic, recent studies have suggested that measured TCP/IP and ATM WAN …

For univariate forecasting, there are various statistical models and computational algorithms
available. In real-world exercises, too many choices can create difficulties in selecting the …