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A practical method for outlier detection in autoregressive time series modelling |
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| Authors: | M C Hau H Tong |
| Institution: | (1) Dept. of Statistics, University of Wisconsin, Madison, Wisconsin, USA;(2) Inst. of Mathematics, University Canterbury, Cornwallis Building, CT2 7NF Kent, U.K. |
| Abstract: | 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. |
| Keywords: | Hat matrix Mahalanobis distance Additive outliers Innovation outliers Influential data Autoregressive models Threshold autoregression Lake Huron |
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