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• W2015217008 abstract "Finding patterns is a common task in time series analysis which has gained a lot of attention across many fields. A multitude of similarity measures have been introduced to perform pattern searches. The accuracy of such measures is often evaluated objectively using a one nearest neighbor classification (1NN) on labeled time series or through clustering. Prior work often disregards the subjective similarity of time series which can be pivotal in systems where a user specified pattern is used as input and a similarity-based ranking is expected as output (query-by-example). In this paper, we describe how a human-annotated ranking based on real-world queries and datasets can be created using simple crowdsourcing tasks and use this ranking as ground-truth to evaluate the perceived accuracy of existing time series similarity measures. Furthermore, we show how different sampling strategies and time series representations of pen-drawn queries effect the precision of these similarity measures and provide a publicly available dataset which can be used to optimize existing and future similarity search algorithms." @default.
• W2015217008 created "2016-06-24" @default.
• W2015217008 creator A5064858317 @default.
• W2015217008 creator A5069672953 @default.
• W2015217008 date "2015-03-18" @default.
• W2015217008 modified "2023-09-27" @default.
• W2015217008 title "Evaluating Subjective Accuracy in Time Series Pattern-Matching Using Human-Annotated Rankings" @default.
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• W2015217008 doi "https://doi.org/10.1145/2678025.2701379" @default.
• W2015217008 hasPublicationYear "2015" @default.
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