Time series: theory and methods
Time series: theory and methods
Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Digital processing of random signals: theory and methods
Digital processing of random signals: theory and methods
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
IEEE/ACM Transactions on Networking (TON)
Change-point detection in long-memory processes
Journal of Multivariate Analysis
Self-Similar Network Traffic and Performance Evaluation
Self-Similar Network Traffic and Performance Evaluation
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
IEEE/ACM Transactions on Networking (TON)
Forecasting network traffic using FARIMA models with heavy tailed innovations
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Non-Gaussian and Long Memory Statistical Characterizations for Internet Traffic with Anomalies
IEEE Transactions on Dependable and Secure Computing
IEEE Transactions on Signal Processing
Cluster processes: a natural language for network traffic
IEEE Transactions on Signal Processing
Blind high-resolution localization and tracking of multiplefrequency hopped signals
IEEE Transactions on Signal Processing
A statistical test for the time constancy of scaling exponents
IEEE Transactions on Signal Processing
Joint hop timing and frequency estimation for collision resolution in FH networks
IEEE Transactions on Wireless Communications
A wavelet-based joint estimator of the parameters of long-range dependence
IEEE Transactions on Information Theory
Multiple-access interference processes are self-similar in multimedia CDMA cellular networks
IEEE Transactions on Information Theory
Theorems and fallacies in the theory of long-range-dependent Processes
IEEE Transactions on Information Theory
Nested auto-regressive processes for MPEG-encoded video traffic modeling
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 35.68 |
This paper addresses the problem of changepoint detection in FARIMA processes. The received signal is modeled as a FARIMA process, with abrupt changes in the Hurst and ARMA parameters. The proposed changepoint detection method first estimates the model parameters over small segments. The changes are then detected in the parameter vector sequence by minimizing an appropriate least-squares criterion. The cases of known, as well as unknown, number of changes are investigated. Dynamic programming is used to solve this optimization problem. A theoretical analysis of the statistical properties of the changepoint estimates is provided. Simulation results on synthetic data and real network traffic data are presented.