Exceptional events in a time series are observations which can be regarded as qualitatively significant anomalies. The detection of such events is a interesting problem in several domains, in particular for the generation of alarms in clinical microbiology. In this paper we propose an approach to the detection of exceptional events based on model selection. For each mathematical form of a model, we choose the parameters of the model by maximum likelihood techniques. Then we select, among the resulting instantiated models, the model which minimizes the mean square error. An exceptional event is detected with an assigned probability, if an observation lies outside the forecasting region defined by the selected model and a confidence interval.

Cellarosi G., Lodi S., Sartori C. (2002). Detecting outbreaks by time series analysis. Los Alamitos, California : IEEE Computer Society [10.1109/CBMS.2002.1011371].

Detecting outbreaks by time series analysis

Cellarosi G.;Lodi S.;Sartori C.
2002

Abstract

Exceptional events in a time series are observations which can be regarded as qualitatively significant anomalies. The detection of such events is a interesting problem in several domains, in particular for the generation of alarms in clinical microbiology. In this paper we propose an approach to the detection of exceptional events based on model selection. For each mathematical form of a model, we choose the parameters of the model by maximum likelihood techniques. Then we select, among the resulting instantiated models, the model which minimizes the mean square error. An exceptional event is detected with an assigned probability, if an observation lies outside the forecasting region defined by the selected model and a confidence interval.
2002
Proceedings of the IEEE Symposium on Computer-Based Medical Systems
159
164
Cellarosi G., Lodi S., Sartori C. (2002). Detecting outbreaks by time series analysis. Los Alamitos, California : IEEE Computer Society [10.1109/CBMS.2002.1011371].
Cellarosi G.; Lodi S.; Sartori C.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/906915
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