Time series data is ubiquitous across various domains such as finance,
healthcare, and manufacturing, but their properties can vary significantly
depending on the domain they originate from. The ability to perform
Content-based Time Series Retrieval (CTSR) is crucial for identifying unknown
time series examples. However, existing CTSR works typically focus on
retrieving time series from a single domain database, which can be inadequate
if the user does not know the source of the query time series. This limitation
motivates us to investigate the CTSR problem in a scenario where the database
contains time series from multiple domains. To facilitate this investigation,
we introduce a CTSR benchmark dataset that comprises time series data from a
variety of domains, such as motion, power demand, and traffic. This dataset is
sourced from a publicly available time series classification dataset archive,
making it easily accessible to researchers in the field. We compare several
popular methods for modeling and retrieving time series data using this
benchmark dataset. Additionally, we propose a novel distance learning model
that outperforms the existing methods. Overall, our study highlights the
importance of addressing the CTSR problem across multiple domains and provides
a useful benchmark dataset for future research.

我们研究了内容基于时间序列检索（CTSR）问题，针对包含多个领域的数据库提出了一个基准数据集，并比较了几种常见的时间序列建模和检索方法。鉴于这一问题的重要性，我们提出了一种优于现有方法的新型距离学习模型。研究总结了跨多个领域解决 CTSR 问题的重要性，并为未来研究提供了有用的基准数据集。

时间宝藏：基于内容的时序检索系统用于发现洞见

Temporal Treasure Hunt: Content-based Time Series Retrieval System for  Discovering Insights

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.

我们提出了一种有效且高效的基于内容的时间序列检索模型，超越了其他模型，在提供合理的推理运行时间的同时，展示了解决业务问题的能力。