mlsanityData to 7 Oct 2026

VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting

arXiv 2405.11470 ↗First posted 19 May 2024cs.LG cs.AIcited by 12

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Abstract (arXiv)

Multivariate time series (MTS) forecasting has been extensively applied across diverse domains, such as weather prediction and energy consumption. However, current studies still rely on the vanilla point-wise self-attention mechanism to capture cross-variable dependencies, which is inadequate in extracting the intricate cross-correlation implied between variables. To fill this gap, we propose Variable Correlation Transformer (VCformer), which utilizes Variable Correlation Attention (VCA) module to mine the correlations among variables. Specifically, based on the stochastic process theory, VCA calculates and integrates the cross-correlation scores corresponding to different lags between queries and keys, thereby enhancing its ability to uncover multivariate relationships. Additionally, inspired by Koopman dynamics theory, we also develop Koopman Temporal Detector (KTD) to better address the non-stationarity in time series. The two key components enable VCformer to extract both multivariate correlations and temporal dependencies. Our extensive experiments on eight real-world datasets demonstrate the effectiveness of VCformer, achieving top-tier performance compared to other state-of-the-art baseline models. Code is available at this repository: https://github.com/CSyyn/VCformer.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 22 of its 42 references that are in the map. First posted on arXiv 19 May 2024.

Builds on

22 of 42 references.

References · 22

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  4. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  5. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  6. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  7. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  8. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  9. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  10. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  11. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
  12. 2017Deep learning for universal linear embeddings of nonlinear dynamicscited by 491
10 more
  1. 2022Transformers in Time Series: A Surveycited by 489
  2. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 385
  3. 2021Modern Koopman Theory for Dynamical Systemscited by 269
  4. 2019Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecastingcited by 161
  5. 2023Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictorscited by 110
  6. 2023The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecastingcited by 100
  7. 2019Learning Compositional Koopman Operators for Model-Based Controlcited by 67
  8. 2023DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Predictioncited by 49
  9. 2020Deep Learning of Koopman Representation for Controlcited by 25
  10. 2023Compressed Interaction Graph based Framework for Multi-behavior Recommendationcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 7; 2026: 2.

12 papers.

  1. 2025A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspectivecited by 58
  2. 2025CC-Time: Cross-Model and Cross-Modality Time Series Forecastingcited by 6
  3. 2024Sequential Order-Robust Mamba for Time Series Forecastingcited by 5
  4. 2026From Text to Forecasts: Bridging Modality Gap with Temporal Evolution Semantic Spacecited by 3
  5. 2024TiVaT: A Transformer with a Single Unified Mechanism for Capturing Asynchronous Dependencies in Multivariate Time Series Forecastingcited by 1
  6. 2024A Comparative Study of Pruning Methods in Transformer-based Time Series Forecastingcited by 1
  7. 2025Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingnesscited by 1
  8. 2026What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependenciescited by 1
  9. 2025CRAFT: Time Series Forecasting with Cross-Future Behavior Awarenesscited by 0
  10. 2025Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated Datacited by 0
  11. 2025CrossLag: Predicting Major Dengue Outbreaks with a Domain Knowledge Informed Transformercited by 0
  12. 2025InvDec: Inverted Decoder for Multivariate Time Series Forecasting with Separated Temporal and Variate Modelingcited by 0