Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
SIAM Journal on Scientific Computing
Simulation of stationary Gaussian vector fields
Statistics and Computing
Sketch-based change detection: methods, evaluation, and applications
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
Live E! Project: Establishment of Infrastructure Sharing Environmental Information
SAINT-W '07 Proceedings of the 2007 International Symposium on Applications and the Internet Workshops
The hitchhiker's guide to successful wireless sensor network deployments
Proceedings of the 6th ACM conference on Embedded network sensor systems
New Introduction to Multiple Time Series Analysis
New Introduction to Multiple Time Series Analysis
Testing fractal connectivity in multivariate long memory processes
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
IEEE Transactions on Information Theory
The simulation of random vector time series with given spectrum
Mathematical and Computer Modelling: An International Journal
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A fast and exact procedure for the numerical synthesis of stationary multivariate Gaussian time series with a priori prescribed and well controlled auto- and cross-covariance functions is proposed. It is based on extending the circulant embedding technique to the multivariate case and can be viewed as a modification and variation around the Chan and Wood algorithm proposed earlier to solve the same problem. The procedure is shown to yield time series possessing exactly the desired covariance structure, when sufficient conditions are satisfied. Such conditions are discussed theoretically and examined on several examples of multivariate time series models. Issues related to prescribing a priori the spectral structure rather than the covariance one are also discussed. Matlab routines implementing this procedure are publicly available at http://www.hermir.org.