Temporal Treasure Hunt: Content-based Time Series Retrieval System for
  Discovering Insights
Chin-Chia Michael Yeh Huiyuan Chen Xin Dai Yan Zheng Yujie Fan Vivian Lai Junpeng Wang Audrey Der Zhongfang Zhuang Liang Wang Wei Zhang
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.