mlsanityData to 14 Oct 2026

DGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting

arXiv 2405.08440 ↗First posted 14 May 2024cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Multivariate time series forecasting tasks are usually conducted in a channel-dependent (CD) way since it can incorporate more variable-relevant information. However, it may also involve a lot of irrelevant variables, and this even leads to worse performance than the channel-independent (CI) strategy. This paper combines the strengths of both strategies and proposes the Deep Graph Clustering Transformer (DGCformer) for multivariate time series forecasting. Specifically, it first groups these relevant variables by a graph convolutional network integrated with an autoencoder, and a former-latter masked self-attention mechanism is then considered with the CD strategy being applied to each group of variables while the CI one for different groups. Extensive experimental results on eight datasets demonstrate the superiority of our method against state-of-the-art models, and our code will be publicly available upon acceptance.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 19 of its 39 references that are in the map. First posted on arXiv 14 May 2024.

Builds on

19 of 39 references.

References · 19

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7 more
  1. 2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Predictioncited by 245
  2. 2016Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoderscited by 220
  3. 2020Structural Deep Clustering Networkcited by 145
  4. 2023The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecastingcited by 101
  5. 2023DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Predictioncited by 49
  6. 2019DSTP-RNN: a dual-stage two-phase attention-based recurrent neural networks for long-term and multivariate time series predictioncited by 20
  7. 2018Multivariate Bayesian Structural Time Series Modelcited by 11

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspectivecited by 58
  2. 2026CoRA: Boosting Time Series Foundation Models for Multivariate Forecasting through Correlation-aware Adaptercited by 7
  3. 2025PENGUIN: Enhancing Transformer with Periodic-Nested Group Attention for Long-term Time Series Forecastingcited by 3
  4. 2026A Scalable Multi-Task Model for Virtual Sensorscited by 0