DETECTING CHAOS IN A NOISY TIME-SERIES
UNSPECIFIED (1993) DETECTING CHAOS IN A NOISY TIME-SERIES. PROCEEDINGS OF THE ROYAL SOCIETY OF LONDON SERIES B-BIOLOGICAL SCIENCES, 253 (1338). pp. 239-244. ISSN 0962-8452Full text not available from this repository.
We propose a new method for detecting low-dimensional chaotic time series when there is dynamical noise present. The method identifies the sign of the largest Liapunov exponent and thus the presence or absence of chaos. It also shows when it is possible to assign a value to the exponent. This approach can work for short time series of only 500 points. We analyse several real time series including chickenpox and measles data from New York City. For model systems it correctly identifies important spatial scales at which noise and nonlinear effects are important. We propose a further technique for estimating the level of noise in real time series if it is difficult to detect by the former method.
|Item Type:||Journal Article|
|Subjects:||Q Science > QH Natural history > QH301 Biology|
|Journal or Publication Title:||PROCEEDINGS OF THE ROYAL SOCIETY OF LONDON SERIES B-BIOLOGICAL SCIENCES|
|Publisher:||ROYAL SOC LONDON|
|Date:||22 September 1993|
|Number of Pages:||6|
|Page Range:||pp. 239-244|
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