Simulation of multivariate non-gaussian autoregressive time series with given autocovariance and marginals

Simulation Modelling Practice and Theory

Volumn 44, Issue , 2014, Pages 42-53

  Kugiumtzis, Dimitris ^a   Bora Senta, Efthimia ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Autocorrelation; Gaussian time series; Non Gaussian time series; Randomization test; Stochastic simulation

Indexed keywords

AUTO REGRESSIVE PROCESS; AUTOCORRELATION MATRIX; AUTOREGRESSIVE TIME SERIES; GAUSSIAN TIME SERIES; MARGINAL DISTRIBUTION; NON-GAUSSIAN; RANDOMIZATION TESTS; STOCHASTIC SIMULATIONS;

AUTOCORRELATION; GAUSSIAN DISTRIBUTION; MATHEMATICAL TRANSFORMATIONS; STOCHASTIC MODELS;

GAUSSIAN NOISE (ELECTRONIC);

EID: 84898073633     PISSN: 1569190X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.simpat.2014.03.001     Document Type: Article

References (45)

1. M.C. Cario, and B.L. Nelson Autoregressive to anything: time-series input processes for simulation Oper. Res. Lett. 19 1996 51 58
2. B. Biller, and B.L. Nelson Fitting time-series input processes for simulation Oper. Res. 53 3 2005
3. M.E. Kuhl, J.S. Ivy, E.K. Lada, N.M. Steiger, M.A. Wagner, and J.R. Wilson Univariate input models for stochastic simulation J. Simul. 4 2010 81 97
4. B. Biller Multivariate Input Modeling Wiley Encyclopedia of Operations Research and Management Science 2010 John Wiley & Sons, Inc.
5. W.H. Ip, R. Fung, and K.W. Keung An investigation of stochastic analysis of flexible manufacturing systems simulation Int. J. Adv. Manuf. Technol. 15 1999 244 250
6. M. He, L. Zhao, and W.B. Powell Approximate dynamic programming algorithms for optimal dosage decisions in controlled ovarian hyperstimulation Eur. J. Oper. Res. 222 2 2012 328 340
7. J. Kriege, and P. Buchholz Correlated phase-type distributed random numbers as input models for simulations Perform. Eval. 68 11 2011 1247 1260
8. L.W. Schruben, D.I. Singham, Simulating multivariate time series using flocking, in: Simulation Conference (WSC), Proceedings of the 2010 Winter, 2010, pp. 1048-1054.
9. Y. Cai Multi-variate time-series simulation J. Time Ser. Anal. 32 2011 566 -579
10. S. Das, R. Ghanem, and S. Finette Polynomial chaos representation of spatio-temporal random fields from experimental measurements J. Comput. Phys. 228 23 2009 8726 8751
11. J. Theiler, S. Eubank, A. Longtin, and B. Galdrikian Testing for nonlinearity in time series: the method of surrogate data Physica D 58 1992 77 94
12. T. Schreiber, and A. Schmitz Improved surrogate data for nonlinearity tests Phys. Rev. Lett. 77 4 1996 635 638
13. T. Schreiber, and A. Schmitz Surrogate time series Physica D 142 3-4 2000 346 382
14. D. Kugiumtzis Statically transformed autoregressive process and surrogate data test for nonlinearity Phys. Rev. E 66 2002 025201
15. D. Kugiumtzis Surrogate data test on time series A. Soofi, L. Cao, Modelling and Forecasting Financial Data, Techniques of Nonlinear Dynamics 2002 Kluwer Academic Publishers 267 282 (Chapter 12)
16. M. Small, C.K. Tse, Determinism in financial time series, Stud. Nonlinear Dyn. Econom., vol. 7, no. (3).
17. A. Das, and P. Das Chaotic analysis of the foreign exchange rates Appl. Math. Comput. 185 1 2007 388 396
18. G. Pavlos, M. Athanasiu, D. Kugiumtzis, N. Hantzigeorgiu, A.G. Rigas, and E.T. Sarris Nonlinear analysis of magnetospheric data. Part I. Geometric characteristics of the AE index time series and comparison with nonlinear surrogate data Nonlinear Processes Geophys. 6 1999 51 65
19. G. Pavlos, D. Kugiumtzis, M. Athanasiu, N. Hantzigeorgiu, D. Diamadidis, and E.T. Sarris Nonlinear analysis of magnetospheric data. Part II. Dynamic characteristics of the AE index time series and comparison with nonlinear surrogate data Nonlinear Processes Geophys. 6 1999 79 98
20. C.J. Keylock Characterizing the structure of nonlinear systems using gradual wavelet reconstruction Nonlinear Processes Geophys. 17 2010 615 632
21. N. Pradhan, and P.K. Sadasivan Relevance of surrogate-data testing in electroencephalogram analysis Phys. Rev. E 53 3 1996 2684 2692
22. F. Mormann, T. Kreuz, R.G. Rieke, C. Andrzejak, A. Kraskov, P. David, C.E. Elger, and K. Lehnertz On the predictability of epileptic seizures Clin. Neurophysiol. 116 3 2005 569 587
23. K.T. Dolan, and A. Neiman Surrogate analysis of coherent multichannel data Phys. Rev. E 65 2002 026108
24. R.G. Andrzejak, A. Kraskov, H. Stögbauer, F. Mormann, and T. Kreuz Bivariate surrogate techniques: necessity, strengths, and caveats Phys. Rev. E 68 2003 066202
25. L. Faes, A. Porta, and G. Nollo Testing frequency-domain causality in multivariate time series IEEE Trans. Biomed. Eng. 57 8 2010 1897 1906
26. S.T. Li, and J.L. Hammond Generation of pseudorandom numbers with specified univariate distributions and correlation coefficients IEEE Trans. Syst., Man Cyber. 5 1975 557 561
27. M.C. Cario, and B.L. Nelson Numerical methods for fitting and simulating autoregressive-to-anything processes INFORMS J. Comput. 10 1 1998 72 81
28. B. Biller, and B.L. Nelson Evaluation of the ARTAFIT method for fitting time-series input processes for simulation INFORMS J. Comput. 20 3 2008 485 498
29. B. Biller, and B.L. Nelson Modeling and generating multivariate time-series input processes using a vector autoregressive technique ACM Trans. Model. Comput. Simul. 13 3 2003 211 237
30. C.J. Keylock A wavelet-based method for surrogate data generation Physica D 225 2 2007 219 228
31. D. Kugiumtzis Surrogate data test for nonlinearity including non-monotonic transforms Phys. Rev. E 62 1 2000 25 28
32. D. Kugiumtzis, and E. Bora-Senta Normal correlation coefficient of non-normal variables using piece-wise linear approximation Comput. Statist. 25 4 2010 645 662
33. J. Theiler, and D. Prichard Constrained realization Monte-Carlo method for hypothesis testing Physica D 94 1996 221 235
34. J.M. Halley, and D. Kugiumtzis Nonparametric testing of variability and trend in some climatic records Climatic Change 109 3-4 2011 549 568
35. W.W.S. Wei Time Series Analysis Univariate & Multivariate Methods second ed. 2006 Addison-Wesley
36. H. Chen Initialization for NORTA: generation of random vectors with specified marginals and correlations INFORMS J. Comput. 13 4 2001 312 331
37. D. Kugiumtzis Evaluation of surrogate and bootstrap tests for nonlinearity in time series Stud. Nonlinear Dyn. Econom. 12 4 2008
38. K.H. Zou, and W.J. Hall On estimating a transformation correlation coefficient J. Appl. Statist. 29 5 2002 745 760
39. R.L. Burden, and J.D. Faires Numerical Analysis seventh ed. 2005 Thomson Brooks/Cole
40. M.G. Kendall, and A. Stuart The Advanced Theory of Statistics fourth ed. Inference and Relationship vol. 2 1979 Griffin London
41. S. Ghosh, and S.G. Henderson Chessboard distributions and random vectors with specified marginals and covariance matrix Oper. Res. 50 5 2002 820 834
42. W. Whitt Bivariate distributions with given marginals Ann. Statist. 4 1976 1280 1289
43. N. Higham Computing the nearest correlation matrix - a problem from finance IMA J. Numer. Anal. 22 3 2002 329 -343
44. H. Lütkepohl New Introduction to Multiple Time Series Analysis 2005 Springer-Verlag Berlin, Heidelberg
45. B. Schelter, M. Winterhalder, B. Hellwig, B. Guschlbauer, C.H. Lücking, and J. Timmer Direct or indirect Graphical models for neural oscillators J. Physiol.-Paris 99 1 2006 37 46