  Time series often contain outliers and level shifts or structural changes.
These unexpected events are of the utmost importance in fraud detection, as
they may pinpoint suspicious transactions. The presence of such unusual events
can easily mislead conventional time series analysis and yield erroneous
conclusions. In this paper we provide a unified framework for detecting
outliers and level shifts in short time series that may have a seasonal
pattern. The approach combines ideas from the FastLTS algorithm for robust
regression with alternating least squares. The double wedge plot is proposed, a
graphical display which indicates outliers and potential level shifts. The
methodology was developed to detect potential fraud cases in time series of
imports into the European Union, and is illustrated on two such series.
