An Efficient Content-based Time Series Retrieval System

TL;DR

This paper introduces a high-capacity, efficient content-based time series retrieval system capable of real-time similarity measurement across diverse domains, demonstrated on transaction data.

Contribution

The paper presents a novel CTSR model that outperforms existing methods in accuracy and efficiency for diverse, real-time time series retrieval tasks.

Findings

The proposed model achieves superior retrieval accuracy.

It provides faster similarity computations suitable for real-time use.

Demonstrated effectiveness on in-house transaction data.

Abstract

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.