## Detection of chaotic determinism in stochastic short time series

Research output: Contribution to journal › Conference article › Research › peer-review

#### Standard

**Detection of chaotic determinism in stochastic short time series.** / Chon, K. H.; Kanters, J. K.; Iyengar, N.; Cohen, R. J.; Holstein-Rathlou, N. H.

Research output: Contribution to journal › Conference article › Research › peer-review

#### Harvard

*Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings*, vol. 1, pp. 275-277.

#### APA

*Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings*,

*1*, 275-277.

#### Vancouver

#### Author

#### Bibtex

}

#### RIS

TY - GEN

T1 - Detection of chaotic determinism in stochastic short time series

AU - Chon, K. H.

AU - Kanters, J. K.

AU - Iyengar, N.

AU - Cohen, R. J.

AU - Holstein-Rathlou, N. H.

PY - 1997/12/1

Y1 - 1997/12/1

N2 - We have developed an algorithm based on the nonlinear autoregressive (NAR) model which is very accurate in determining whether chaotic determinism is present in a noisy time series and is effective even for a time series with as few as 500 data points. The algorithm is based on fitting a deterministic and stochastic nonlinear autoregressive (NAR) model to the time series, followed by an estimation of the Lyapunov exponents of the resultant fitted model. The major benefits of this algorithm are: 1) it provides accurate parameter estimation with as few as 500 data points, 2) it is accurate down to signal-to-noise ratios of -9 dB (variance of the noise is approximately 2.9 times greater than the variance of the signal), and 3) it allows characterization of the dynamics of the system, and thus prediction of future states of the system. The advantages of the developed algorithm allow this method to be superior to the conventional algorithms for calculating Lyapunov exponents.

AB - We have developed an algorithm based on the nonlinear autoregressive (NAR) model which is very accurate in determining whether chaotic determinism is present in a noisy time series and is effective even for a time series with as few as 500 data points. The algorithm is based on fitting a deterministic and stochastic nonlinear autoregressive (NAR) model to the time series, followed by an estimation of the Lyapunov exponents of the resultant fitted model. The major benefits of this algorithm are: 1) it provides accurate parameter estimation with as few as 500 data points, 2) it is accurate down to signal-to-noise ratios of -9 dB (variance of the noise is approximately 2.9 times greater than the variance of the signal), and 3) it allows characterization of the dynamics of the system, and thus prediction of future states of the system. The advantages of the developed algorithm allow this method to be superior to the conventional algorithms for calculating Lyapunov exponents.

UR - http://www.scopus.com/inward/record.url?scp=0031294277&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0031294277

VL - 1

SP - 275

EP - 277

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

SN - 0589-1019

T2 - Proceedings of the 1997 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 30 October 1997 through 2 November 1997

ER -

ID: 204299799