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Time series are one of the most common data types in nature. Given this fact, there are dozens of query-by-sketching/ query-by-example/ query-algebra systems proposed to allow users to search large time series collections. However, none of these systems have seen widespread adoption. We argue that there are two reasons why this is so. The first reason is that these systems are often complex and unintuitive, requiring the user to understand complex syntax/interfaces to construct high-quality queries. The second reason is less well appreciated. The expressiveness of most query-by-content systems is surprisingly limited. There are well defined, simple queries that cannot be answered by any current query-by-content system, even if it uses a state-of-the-art distance measure such as Dynamic Time Warping. In this work, we propose a natural language search mechanism for searching time series. We show that our system is expressive, intuitive, and requires little space and time overhead. Because our system is text-based, it can leverage decades of research text retrieval, including ideas such as relevance feedback. Moreover, we show that our system subsumes both motif/discord discovery and most existing query-by-content systems in the literature. We demonstrate the utility of our system with case studies in domains as diverse as animal motion studies, medicine and industry.

Putting the Human in the Time Series Analytics Loop