Abstract
The detection of multiple outliers in time series is a cumbersome task because of the large number of combinations of the candidate locations. A genetic algorithm is proposed for the identification of additive and innovation outliers. The objective function depends on both the likelihood function and the number of outliers. Some case studies show that the algorithm is effective in detecting outliers’ location and type and in estimating their size.
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