A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing. For example, users seeking to learn more about the source of a time series can submit the time series as a query to the CTSR system and retrieve a list of relevant time series with associated metadata. By analyzing the retrieved metadata, users can gather more information about the source of the time series. Because the CTSR system is required to work with time series data from diverse domains, it needs a high-capacity model to effectively measure the similarity between different time series. On top of that, the model within the CTSR system has to compute the similarity scores in an efficient manner as the users interact with the system in real-time. In this paper, we propose an effective and efficient CTSR model that outperforms alternative models, while still providing reasonable inference runtimes. To demonstrate the capability of the proposed method in solving business problems, we compare it against alternative models using our in-house transaction data. Our findings reveal that the proposed model is the most suitable solution compared to others for our transaction data problem.

翻译：内容型时间序列检索（CTSR）系统是一种信息检索系统，用于用户与来自金融、医疗和制造等多个领域的时间序列数据进行交互。例如，希望深入了解时间序列来源的用户可将该时间序列作为查询提交至CTSR系统，并检索到相关时间序列及其关联元数据的列表。通过分析检索到的元数据，用户能够收集关于该时间序列来源的更多信息。由于CTSR系统需要处理来自不同领域的时间序列数据，它需要高容量模型来有效衡量不同时间序列之间的相似性。在此基础上，CTSR系统中的模型必须能够高效计算相似度得分，以满足用户与系统实时交互的需求。本文提出了一种高效且有效的CTSR模型，该模型在保持合理推理运行时间的同时，性能优于其他备选模型。为验证所提方法解决业务问题的能力，我们使用内部交易数据将其与替代模型进行对比。研究结果表明，针对我们的交易数据问题，所提模型是最优解决方案。