mlsanityData to 7 Oct 2026

TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting

arXiv 2307.14680 ↗First posted 27 Jul 2023cs.LGcited by 4

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Abstract (arXiv)

Time series forecasting lies at the core of important real-world applications in many fields of science and engineering. The abundance of large time series datasets that consist of complex patterns and long-term dependencies has led to the development of various neural network architectures. Graph neural network approaches, which jointly learn a graph structure based on the correlation of raw values of multivariate time series while forecasting, have recently seen great success. However, such solutions are often costly to train and difficult to scale. In this paper, we propose TimeGNN, a method that learns dynamic temporal graph representations that can capture the evolution of inter-series patterns along with the correlations of multiple series. TimeGNN achieves inference times 4 to 80 times faster than other state-of-the-art graph-based methods while achieving comparable forecasting performance

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 36 references that are in the map. First posted on arXiv 27 Jul 2023.

Builds on

23 of 36 references.

References · 23

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11 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with pythoncited by 0
  2. 2026TimeGNN-Augmented Hybrid-Action MARL for Fine-Grained Task Partitioning and Energy-Aware Offloading in MECcited by 0
  3. 2026SDMixer: Sparse Dual-Mixer for Time Series Forecastingcited by 0
  4. 2026Experiments in Agentic AI for Sciencecited by 0