An Efficient Content-based Time Series Retrieval System
Chin-Chia Michael Yeh Huiyuan Chen Xin Dai Yan Zheng Junpeng Wang Vivian Lai Yujie Fan Audrey Der Zhongfang Zhuang Liang Wang Wei Zhang Jeff M. Phillips
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.