On the self-similar nature of Ethernet traffic
SIGCOMM '93 Conference proceedings on Communications architectures, protocols and applications
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
IEEE/ACM Transactions on Networking (TON)
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Identifying distinctive subsequences in multivariate time series by clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Spatial join selectivity using power laws
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Self-Similar Layered Hidden Markov Models
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Testing the Gaussian assumption for self-similar teletraffic models
SPWHOS '97 Proceedings of the 1997 IEEE Signal Processing Workshop on Higher-Order Statistics (SPW-HOS '97)
Time series models for internet traffic
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 2
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Recently there are a handful study and research on observing self-similarity and fractals in natural structures and scientific database such as traffic data from networks. However, there are few works on employing such information for predictive modeling, data mining and knowledge discovery. In this paper we study, analyze our experiments and observation of self-similar structure embedded in Network data for prediction through Self Similar Layered Hidden Markov Model (SSLHMM). SSLHMM is a novel alternative of Hidden Markov Models (HMM) which proven to be useful in a variety of real world applications. SSLHMM leverage HMM power and extend such capability to self-similar structures and exploit this property to reduce the complexity of predictive modeling process. We show that SSLHMM approach can captures self-similar information and provides more accurate and interpretable model comparing to conventional techniques.