Nonlinear slight parameter changes detection : A forecasting approach
In many biological systems it is crucial to detect changes, as accurate as possible, in the parameters that govern their dynamics. In this work we propose a new method to perform an online automatic detection of such changes, making use of a well known nonlinear fore- casting algorithm. The approach...
| Autores principales: | , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2012
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/123918 https://41jaiio.sadio.org.ar/sites/default/files/15_AST_2012.pdf |
| Aporte de: |
| id |
I19-R120-10915-123918 |
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| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas Nonlinear event detection Nonlinear forecasting Prediction error |
| spellingShingle |
Ciencias Informáticas Nonlinear event detection Nonlinear forecasting Prediction error Sulam, Jeremias Schlotthauer, Gastón Torres, María E. Nonlinear slight parameter changes detection : A forecasting approach |
| topic_facet |
Ciencias Informáticas Nonlinear event detection Nonlinear forecasting Prediction error |
| description |
In many biological systems it is crucial to detect changes, as accurate as possible, in the parameters that govern their dynamics. In this work we propose a new method to perform an online automatic detection of such changes, making use of a well known nonlinear fore- casting algorithm. The approach takes advantage of the characterization of an interval of a signal by the reconstruction of its phase space through time-delay embedding. To this end, the optimal delay and embedding dimension are estimated, and a method is proposed for determining the forecasting parameters, after which it is possible to predict future values of the studied signal. In this novel approach the method is used as a way of detecting changes in the dynamics of a system, given that the forecast is performed using a template of the signal where its parameters remain constant. At this point, the measure of the prediction error is used to detect a change in the dynamics of the system. We also propose a second estimator of this change, namely prediction failure, which is a stronger binary estimator of change in the dynamics. The results are analyzed by a cumulative sum algorithm (CUSUM ) to obtain a detection point. In order to test their behavior, both methods are applied to deterministic discrete and continuos synthesized data, and to a simulated biological model. |
| format |
Objeto de conferencia Objeto de conferencia |
| author |
Sulam, Jeremias Schlotthauer, Gastón Torres, María E. |
| author_facet |
Sulam, Jeremias Schlotthauer, Gastón Torres, María E. |
| author_sort |
Sulam, Jeremias |
| title |
Nonlinear slight parameter changes detection : A forecasting approach |
| title_short |
Nonlinear slight parameter changes detection : A forecasting approach |
| title_full |
Nonlinear slight parameter changes detection : A forecasting approach |
| title_fullStr |
Nonlinear slight parameter changes detection : A forecasting approach |
| title_full_unstemmed |
Nonlinear slight parameter changes detection : A forecasting approach |
| title_sort |
nonlinear slight parameter changes detection : a forecasting approach |
| publishDate |
2012 |
| url |
http://sedici.unlp.edu.ar/handle/10915/123918 https://41jaiio.sadio.org.ar/sites/default/files/15_AST_2012.pdf |
| work_keys_str_mv |
AT sulamjeremias nonlinearslightparameterchangesdetectionaforecastingapproach AT schlotthauergaston nonlinearslightparameterchangesdetectionaforecastingapproach AT torresmariae nonlinearslightparameterchangesdetectionaforecastingapproach |
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Repositorios |
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1764820450523742210 |