Abstract
In this paper we study, from both a theoretical and an experimental perspective, algorithms and data structures to process queries that help in the detection of rare variations over time intervals that occur in time series. Our research is strongly motivated by applications in financial domain.
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Valentim, C., Laber, E.S., Sotelo, D. (2012). Data Structures for Detecting Rare Variations in Time Series. In: Flach, P.A., De Bie, T., Cristianini, N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science(), vol 7524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33486-3_45
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DOI: https://doi.org/10.1007/978-3-642-33486-3_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33485-6
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