We develop a new multiscale modeling framework for characterizing positive-valued data
with long-range-dependent correlations (1/f noise). Using the Haar wavelet transform and a …

Using the CRSP (Center for Research in Security Prices) daily stock return data, we revisit
the question of whether or not actual stock market prices exhibit long-range dependence …

Recent network traffic studies argue that network arrival processes are much more faithfully
modeled using statistically self-similar processes instead of traditional Poisson processes …

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

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 …

Statistical performance evaluation has assumed an increasing amount of importance as we
seek to design more and more sophisticated communication and information processing …

With the price of wireless sensor technologies diminishing rapidly we can expect large
numbers of autonomous sensor networks being deployed in the near future. These sensor …

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 …

Self-similarity and scaling phenomena have dominated Internet traffic analysis for the past
decade. With the identification of long-range dependence (LRD) in network traffic, the …

The only integrative approach to chaos and random fractal theory Chaos and random fractal
theory are two of the most important theories developed for data analysis. Until now, there …