mlsanityData to 7 Oct 2026

Deep Coupling Network For Multivariate Time Series Forecasting

arXiv 2402.15134 ↗First posted 23 Feb 2024cs.LG cs.AIcited by 6

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Abstract (arXiv)

Multivariate time series (MTS) forecasting is crucial in many real-world applications. To achieve accurate MTS forecasting, it is essential to simultaneously consider both intra- and inter-series relationships among time series data. However, previous work has typically modeled intra- and inter-series relationships separately and has disregarded multi-order interactions present within and between time series data, which can seriously degrade forecasting accuracy. In this paper, we reexamine intra- and inter-series relationships from the perspective of mutual information and accordingly construct a comprehensive relationship learning mechanism tailored to simultaneously capture the intricate multi-order intra- and inter-series couplings. Based on the mechanism, we propose a novel deep coupling network for MTS forecasting, named DeepCN, which consists of a coupling mechanism dedicated to explicitly exploring the multi-order intra- and inter-series relationships among time series data concurrently, a coupled variable representation module aimed at encoding diverse variable patterns, and an inference module facilitating predictions through one forward step. Extensive experiments conducted on seven real-world datasets demonstrate that our proposed DeepCN achieves superior performance compared with the state-of-the-art baselines.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 37 of its 70 references that are in the map. First posted on arXiv 23 Feb 2024.

Builds on

37 of 70 references.

References · 37

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25 more
  1. 2016Wide & Deep Learning for Recommender Systemscited by 745
  2. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
  3. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 606
  4. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
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Built on by

Cited by year (2026 to 7 Oct)

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

6 papers.

  1. 2024FilterNet: Harnessing Frequency Filters for Time Series Forecastingcited by 44
  2. 2025SEMPO: Lightweight Foundation Models for Time Series Forecastingcited by 6
  3. 2024Robust Multivariate Time Series Forecasting against Intra- and Inter-Series Transitional Shiftcited by 1
  4. 2025D-CTNet: A Dual-Branch Channel-Temporal Forecasting Network with Frequency-Domain Correctioncited by 0
  5. 2026Multi-Term Fourier Graph Neural Network with Sample Relationship Learning for Enhanced Remaining Useful Life Predictioncited by 0
  6. 2026AdaST: Adaptive Coupling for Spatial-Temporal Forecastingcited by 0