  We address the problem of data-driven pattern identification and outlier
detection in time series. To this end, we use singular value decomposition
(SVD) which is a well-known technique to compute a low-rank approximation for
an arbitrary matrix. By recasting the time series as a matrix it becomes
possible to use SVD to highlight the underlying patterns and periodicities.
This is done without the need for specifying user-defined parameters. From a
data mining perspective, this opens up new ways of analyzing time series in a
data-driven, bottom-up fashion. However, in order to get correct results, it is
important to understand how the SVD-spectrum of a time series is influenced by
various characteristics of the underlying signal and noise. In this paper, we
have extended the work in earlier papers by initiating a more systematic
analysis of these effects. We then illustrate our findings on some real-life
data.
