Visual Support for Rastering of Unequally Spaced Time Series

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Abstract
Preprocessing is a mandatory first step to make data usable for analysis. While in time series analysis many established methods require data that are sampled in regular time intervals, in practice sensors may sample data at varying interval lengths. Time series rastering is the process of aggregating unequally spaced time series into equal interval lengths. In this paper we discuss critical aspects in the context of time series rastering, and we present a visual design which supports the parametrization of the rastering transformation, communicates the introduced uncertainties and quality issues, and facilitates the comparison of alternative rastering outcomes to achieve optimal results.
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Year of Publication
2017
Conference Name
10th International Symposium on Visual Information Communication and Interaction (VINCI)
Number of Pages
Publisher
ACM New York, NY, USA
Conference Location
Bangkok, Thailand
ISBN Number
978-1-4503-5292-5
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URL
http://dl.acm.org/citation.cfm?id=3105984
DOI
10.1145/3105971.3105984
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