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

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arxiv 2307.03759 v3 pith:IA2VIAEA submitted 2023-07-07 cs.LG cs.AI

Ming Jin , Huan Yee Koh , Qingsong Wen , Daniele Zambon , Cesare Alippi , Geoffrey I. Webb , Irwin King , Shirui Pan This is my paper · ORCID
classification cs.LGcs.AI
keywords timeseriesneuralgnn4tsgraphnetworksresearchsurvey
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 8 Pith papers

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    HDT forecasts multivariate time series by generating a discrete coarse trend of the future, then generating finer target tokens conditioned on that predicted trend, outperforming prior methods on five datasets.

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    A forecasting-based anomaly detector whose sensor graph is hard-gated by an LLM-extracted physical-coupling prior and modulated by Pearson correlations outperforms several baselines on SKAB.

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    ClouDens detects cloud telemetry anomalies by partitioning metrics into status/aggregation subsets, building context-aware graphs, and forecasting with ST-GNNs, beating a GRU baseline on IBM Cloud data.

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