mlsanityData to 14 Oct 2026

An Efficient Content-based Time Series Retrieval System

arXiv 2310.03919 ↗First posted 5 Oct 2023cs.IR cs.AI cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 22 of its 51 references that are in the map. First posted on arXiv 5 Oct 2023.

Builds on

22 of 51 references.

References · 22

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10 more
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Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 2.

5 papers.

  1. 2025TRACE: Grounding Time Series in Context for Multimodal Embedding and Retrievalcited by 7
  2. 2024Semantic Feature Learning for Universal Unsupervised Cross-Domain Retrievalcited by 1
  3. 2023Temporal Treasure Hunt: Content-based Time Series Retrieval System for Discovering Insightscited by 0
  4. 2023Ego-Network Transformer for Subsequence Classification in Time Series Datacited by 0
  5. 2025QuiZSF: A Retrieval-Augmented Framework for Zero-Shot Time Series Forecastingcited by 0