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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 paper, 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 th
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