Abstract
This paper extends the forward search technique to the analysis of structural time series data. It provides a series of powerful new forward plots that use information from the whole sample to display the effect of each observation on a wide variety of aspects of the fitted model and shows how the forward search, free from masking and swamping problems, can detect the main underlying features of the series under study (masked multiple outliers, level shifts or transitory changes). The effectiveness of the suggested approach is shown through the analysis of real and simulated data.
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