LASS: a tool for the local analysis of self-similarity

The Hurst parameter H characterizes the degree of long-range dependence (and asymptotic selfsimilarity) in stationary time series. Many methods have been developed for the estimation of H from data. In practice, however, the classical estimation techniques can be severely affected by nonstationary artifacts in the time series. In fact, the assumption that the data can be modeled by a stationary process with a single Hurst exponent H may be unrealistic. This work focuses on practical issues associated with the detection of long-range dependence in Internet traffic data and proposes two tools that can be used to address some of these issues. The first is an animation tool which is used to visualize the local dependence structure. The second is a statistical tool for the local analysis of selfsimilarity (LASS). The LASS tool is designed to handle time series that have long-range dependence and are long enough that some parts are essentially stationary, while others exhibit non-stationari...

• Type: Journal
• Year: 2006
• Where: CSDA
• Authors: Stilian Stoev, Murad S. Taqqu, Cheolwoo Park, George Michailidis, J. S. Marron
• Keywords: CSDA 2006, Local Dependence Structure, Long-range Dependence, Tools