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Cited by 567Time Series Analysis and ForecastingAdvanced Graph Neural NetworksTraffic Prediction and Management Techniques

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

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Jin, Ming, Koh, Huan Yee, Wen, Qingsong, Zambon, Daniele, Alippi, Cesare, Webb, Geoffrey I., King, Irwin, Pan, Shirui·2024·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2024.3443141
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Abstract

Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. With the recent advancements in graph neural networks (GNNs), there has been a surge in GNN-based approaches for time series analysis. These approaches can explicitly model inter-temporal and inter-variable relationships, which traditional and other deep neural network-based methods struggle to do. In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation. Our aim is to guide designers and practitioners to understand, build applications, and advance research of GNN4TS. At first, we provide a comprehensive task-oriented taxonomy of GNN4TS. Then, we present and discuss representative research works and introduce mainstream applications of GNN4TS. A comprehensive discussion of potential future research directions completes the survey. This survey, for the first time, brings together a vast array of knowledge on GNN-based time series research, highlighting foundations, practical applications, and opportunities of graph neural networks for time series analysis.

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Authors
Jin, M., Koh, H. Y., Wen, Q., Zambon, D., Alippi, C., Webb, G. I., King, I., & Pan, S.
Affiliations
  • Griffith University (AU)
  • Monash University (AU)
  • Squirrel AI Learning, Bellevue, WA, USA
  • Dalle Molle Institute for Artificial Intelligence Research (CH)
  • Chinese University of Hong Kong (HK)
ORCID
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2024-12
Volume
46
Issue
12
Pages
10466-10485
ISSN
  • 0162-8828 (ISSN-L)
  • 2160-9292
  • 1939-3539
DOI
10.1109/tpami.2024.3443141
OpenAlex
W4401567681
Cited by
567
References
14
Citations by year
2026: 236 · 2025: 249 · 2024: 73 · 2023: 9
FWCI
100.09
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, other-oa)
  • Griffith Research Online (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
  • National Science Foundation
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (204061)
  • Australian Research Council (FT210100097, DP240101547)
Topics
  • Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
Keywords
graph neural networks, time series analysis, time series forecasting, time series classification, time series imputation, anomaly detection, soft sensors

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