Qute: Query by Text Search for Time Series Data

Query-based similarity search is a useful exploratory tool that has been used in many areas such as music, economics, and biology to find common patterns and behaviors. Existing query-based search systems allow users to search large time series collections, but these systems are not very robust and they often fail to find similar patterns. In this work, we present Qute (Query by Text) a natural language search framework for finding similar patterns in time series. We show that Qute is expressive while having very small space and time overhead. Qute is a text-based search which leverages information retrieval features such as relevance feedback. Furthermore, Qute subsumes motif and discord/anomaly discovery. We demonstrate the utility of Qute with case studies on both animal behavior and human behavior data.