Querying time series based on their relations is a crucial part of multiple time series analysis. By retrieving and understanding time series relations, analysts can easily detect anomalies and validate hypotheses in complex time series datasets. However, current relation extraction approaches, including knowledge- and data-driven ones, tend to be laborious and do not support heterogeneous relations. By conducting a formative study with 11 experts, we concluded 6 time series relations, including correlation, causality, similarity, lag, arithmetic, and meta, and summarized three pain points in querying time series involving these relations. We proposed RelaQ, an interactive system that supports the time series query via relation specifications. RelaQ allows users to intuitively specify heterogeneous relations when querying multiple time series, understand the query results based on a scalable, multi-level visualization, and explore possible relations beyond the existing queries. RelaQ is evaluated with two use cases and a user study with 12 participants, showing promising effectiveness and usability.

翻译：基于关系查询时间序列是多时间序列分析的关键环节。通过获取和理解时间序列关系，分析人员能够轻松检测复杂时间序列数据集中的异常并验证假设。然而，当前的关系提取方法（包括知识驱动和数据驱动方法）往往费力且不支持异构关系。通过对11位专家进行形成性研究，我们归纳出6种时间序列关系（包括相关性、因果性、相似性、时滞、算术关系和元关系），并总结出涉及这些关系的时间序列查询中的三个痛点。我们提出RelaQ交互式系统，支持通过关系规范进行时间序列查询。RelaQ允许用户在查询多个时间序列时直观地指定异构关系，基于可扩展的多层级可视化理解查询结果，并探索现有查询之外的可能关系。通过两个用例和12名参与者的用户研究评估，RelaQ展现出良好的有效性与可用性。