A practical method for outlier detection in autoregressive time series modelling
| Autor: | M C Hau, Howell Tong |
|---|---|
| Rok vydání: | 1989 |
| Předmět: |
H Social Sciences (General)
Mahalanobis distance Environmental Engineering Computer science Mechanical Engineering Ocean Engineering Computational intelligence Function (mathematics) computer.software_genre Data set ComputingMethodologies_PATTERNRECOGNITION Autoregressive model Modeling and Simulation Statistics Outlier Environmental Chemistry HA Statistics Anomaly detection Data mining Time series Safety Risk Reliability and Quality computer General Environmental Science Water Science and Technology |
| Zdroj: | Stochastic Hydrology and Hydraulics. 3:241-260 |
| ISSN: | 1436-3259 0931-1955 |
| Popis: | A practical method is developed for outlier detection in autoregressive modelling. It has the interpretation of a Mahalanobis distance function and requires minimal additional computation once a model is fitted. It can be of use to detect both innovation outliers and additive outliers. Both simulated data and real data re used for illustration, including one data set from water resources. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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