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This paper presents an approach to analyzing two-dimensional temporaldatasets focusing on identifying observations that are significant incalculating the outliers of a scatterplot. We also propose a prototype, calledOutliagnostics, to guide users when interactively exploring abnormalities inlarge time series. Instead of focusing on detecting outliers at each timepoint, we monitor and display the discrepant temporal signatures of each dataentry concerning the overall distributions. Our prototype is designed to handlethese tasks in parallel to improve performance. To highlight the benefits andperformance of our approach, we illustrate and validate the use ofOutliagnostics on real-world datasets of various sizes in different parallelismconfigurations. This work also discusses how to extend these ideas to handletime series with a higher number of dimensions and provides a prototype forthis type of datasets.

Outliagnostics: Visualizing Temporal Discrepancy in Outlying Signatures of Data Entries