mlsanityData to 14 Oct 2026

Learning Gaussian Mixture Representations for Tensor Time Series Forecasting

arXiv 2306.00390 ↗First posted 1 Jun 2023cs.LGcited by 6

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Abstract (arXiv)

Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems involving multi-source spatio-temporal data (e.g., transportation demands and air pollutants). Compared to modeling time series or multivariate time series, which has received much attention and achieved tremendous progress in recent years, tensor time series has been paid less effort. Properly coping with the tensor time series is a much more challenging task, due to its high-dimensional and complex inner structure. In this paper, we develop a novel TTS forecasting framework, which seeks to individually model each heterogeneity component implied in the time, the location, and the source variables. We name this framework as GMRL, short for Gaussian Mixture Representation Learning. Experiment results on two real-world TTS datasets verify the superiority of our approach compared with the state-of-the-art baselines. Code and data are published on https://github.com/beginner-sketch/GMRL.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 26 of its 44 references that are in the map. First posted on arXiv 1 Jun 2023.

Builds on

26 of 44 references.

References · 26

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14 more
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  2. 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 171
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 2.

6 papers.

  1. 2024Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecastingcited by 18
  2. 2024Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learningcited by 9
  3. 2025How Different from the Past? Spatio-Temporal Time Series Forecasting with Self-Supervised Deviation Learningcited by 4
  4. 2024Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecastingcited by 0
  5. 2026Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learningcited by 0
  6. 2026Frequency-Domain Multi-Modality Transportation Modelingcited by 0