  This paper presents an approach to analyzing two-dimensional temporal
datasets focusing on identifying observations that are significant in
calculating the outliers of a scatterplot. We also propose a prototype, called
Outliagnostics, to guide users when interactively exploring abnormalities in
large time series. Instead of focusing on detecting outliers at each time
point, we monitor and display the discrepant temporal signatures of each data
entry concerning the overall distributions. Our prototype is designed to handle
these tasks in parallel to improve performance. To highlight the benefits and
performance of our approach, we illustrate and validate the use of
Outliagnostics on real-world datasets of various sizes in different parallelism
configurations. This work also discusses how to extend these ideas to handle
time series with a higher number of dimensions and provides a prototype for
this type of datasets.
