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• W3036880972 abstract "Sketching is one common approach to query time series data for patterns of interest. Most existing solutions for matching the data with the interaction are based on an empirically modeled similarity function between the user's sketch and the time series data with limited efficiency and accuracy. In this article, we introduce a machine learning based solution for fast and accurate querying of time series data based on a swift sketching interaction. We build on existing LSTM technology (long short-term memory) to encode both the sketch and the time series data in a network with shared parameters. We use data from a user study to let the network learn a proper similarity function. We focus our approach on perceived similarities and achieve that the learned model also includes a user-side aspect. To the best of our knowledge, this is the first data-driven solution for querying time series data in visual analytics. Besides evaluating the accuracy and efficiency directly in a quantitative way, we also compare our solution to the recently published Qetch algorithm as well as the commonly used dynamic time warping (DTW) algorithm." @default.
• W3036880972 created "2020-06-25" @default.
• W3036880972 creator A5000695313 @default.
• W3036880972 creator A5000755776 @default.
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• W3036880972 date "2021-12-01" @default.
• W3036880972 modified "2023-09-27" @default.
• W3036880972 title "Sketch-Based Fast and Accurate Querying of Time Series Using Parameter-Sharing LSTM Networks" @default.
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• W3036880972 doi "https://doi.org/10.1109/tvcg.2020.3002950" @default.
• W3036880972 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32746264" @default.
• W3036880972 hasPublicationYear "2021" @default.
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