Sketch-Based Fast and Accurate Querying of Time Series Using Parameter-Sharing LSTM Networks.

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.