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

By Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I. Webb, Irwin King, Shirui Pan. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023

498 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiaoming Shi
2. Shirui Pan
3. Ming Jin
4. Shiyu Wang

In Signal Processing

1. Xiaoming Shi
2. Lintao Ma
3. Shiyu Wang

Citations in the corpus

• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models (2023, background): “Time series forecasting is a critical capability across many real-world dynamic systems, with applications ranging from demand planning (Leonard, 2001) and inventory optimization (Li et al., 2022) to energy load forecasting (Liu et al., 2023a) and climate modeling (Schneider & Dickinson, 1974).”