@inproceedings{755ebcaec9f6467eb0501a76d0386c7e,
title = "Self-similar traffic prediction using least mean kurtosis",
abstract = "Recent studies of high quality; high resolution traffic measurements have revealed that network traffic appears to be statistically self similar. Contrary to the common belief, aggregating self-similar traffic streams can actually intensify rather than diminish burstiness. Thus, traffic prediction plays an important role in network management. In this paper, least mean kurtosis (LMK), which uses the negated kurtosis of the error signal as the cost function, is proposed to predict the self similar traffic. Simulation results show that the prediction performance is improved greatly over the least mean square (LMS) algorithm.",
author = "Hong Zhao and N. Ansari and Shi, {Y. Q.}",
note = "Publisher Copyright: {\textcopyright} 2003 IEEE.; International Conference on Information Technology: Computers and Communications, ITCC 2003 ; Conference date: 28-04-2003 Through 30-04-2003",
year = "2003",
doi = "10.1109/ITCC.2003.1197554",
language = "English (US)",
series = "Proceedings ITCC 2003, International Conference on Information Technology: Computers and Communications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "352--355",
editor = "Srimani, {Pradip K.} and Emma Regentova and Ray Hashemi and Elaine Lawrence and Mario Cannataro and Amanda Spink and Wolf Bein",
booktitle = "Proceedings - ITCC 2003, International Conference on Information Technology",
address = "United States",
}