SDGF: Fusing Static and Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting

• URL: http://arxiv.org/abs/2509.18135v1
• Date: Sun, 14 Sep 2025 11:23:12 GMT
• Title: SDGF: Fusing Static and Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting
• Authors: Shaoxun Wang, Xingjun Zhang, Qianyang Li, Jiawei Cao, Zhendong Tan,
• Abstract summary: Inter-series correlations are crucial for accurate time series forecasting.<n>These relationships often exhibit complex dynamics across different temporal scales.<n>Existing methods are limited in modeling these multi-scale dependencies.
• Score: 9.027814258970684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inter-series correlations are crucial for accurate multivariate time series forecasting, yet these relationships often exhibit complex dynamics across different temporal scales. Existing methods are limited in modeling these multi-scale dependencies and struggle to capture their intricate and evolving nature. To address this challenge, this paper proposes a novel Static-Dynamic Graph Fusion network (SDGF), whose core lies in capturing multi-scale inter-series correlations through a dual-path graph structure learning approach. Specifically, the model utilizes a static graph based on prior knowledge to anchor long-term, stable dependencies, while concurrently employing Multi-level Wavelet Decomposition to extract multi-scale features for constructing an adaptively learned dynamic graph to capture associations at different scales. We design an attention-gated module to fuse these two complementary sources of information intelligently, and a multi-kernel dilated convolutional network is then used to deepen the understanding of temporal patterns. Comprehensive experiments on multiple widely used real-world benchmark datasets demonstrate the effectiveness of our proposed model.

Related papers

• DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters [50.43534351968113]
  Existing generative time series models do not address the multi-dimensional properties of time series data well.<n>Inspired by Multimodal Diffusion Transformers that integrate textual guidance into video generation, we propose Diffusion Transformers for Time Series (DiTS)
  arXiv  Detail & Related papers  (2026-02-06T10:48:13Z)
• Improving Day-Ahead Grid Carbon Intensity Forecasting by Joint Modeling of Local-Temporal and Cross-Variable Dependencies Across Different Frequencies [3.1953619915392246]
  Accurate forecasting of the grid carbon intensity factor (CIF) is critical for enabling demand-side management and reducing emissions in modern electricity systems.<n>Despite advances in deep learning-based methods, it remains challenging to capture the fine-grained local-temporal dependencies.<n>We propose a novel model that integrates two parallel modules.
  arXiv  Detail & Related papers  (2026-01-10T11:20:55Z)
• FusAD: Time-Frequency Fusion with Adaptive Denoising for General Time Series Analysis [92.23551599659186]
  Time series analysis plays a vital role in fields such as finance, healthcare, industry, and meteorology.<n>FusAD is a unified analysis framework designed for diverse time series tasks.
  arXiv  Detail & Related papers  (2025-12-16T04:34:27Z)
• MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting [51.94256702463408]
  Time series predictability is derived from periodic characteristics at different frequencies.<n>We propose a novel time series forecasting method based on multi-frequency reference series correlation analysis.<n> Experiments on major open and synthetic datasets show state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-11T11:40:14Z)
• TiVaT: A Transformer with a Single Unified Mechanism for Capturing Asynchronous Dependencies in Multivariate Time Series Forecasting [4.733959271565453]
  TiVaT is a novel architecture incorporating a single unified module, a Joint-Axis (JA) attention module.<n>The JA attention module dynamically selects relevant features to particularly capture asynchronous interactions.<n>Extensive experiments demonstrate TiVaT's overall performance across diverse datasets.
  arXiv  Detail & Related papers  (2024-10-02T13:24:24Z)
• PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations [49.80959046861793]
  We present PDETime, a novel LMTF model inspired by the principles of Neural PDE solvers. Our experimentation across seven diversetemporal real-world LMTF datasets reveals that PDETime adapts effectively to the intrinsic nature of the data.
  arXiv  Detail & Related papers  (2024-02-25T17:39:44Z)
• MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting [18.192600104502628]
  Time series data often exhibit diverse intra-series and inter-series correlations. Extensive experiments are conducted on several real-world datasets to showcase the effectiveness of MSGNet.
  arXiv  Detail & Related papers  (2023-12-31T08:23:24Z)
• Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting [0.16492989697868887]
  We introduce a method of representing multivariate signals as nodes in a graph with edges indicating interdependency between them. We leverage graph neural networks (GNN) and attention mechanisms to efficiently learn the underlying relationships within the time series data. The effectiveness of our proposed model is evaluated across various real-world benchmark datasets designed for long-term forecasting tasks.
  arXiv  Detail & Related papers  (2023-11-21T14:24:21Z)
• Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting [50.901984244738806]
  We show how to model the evolutionary and multi-scale interactions of time series. In particular, we first provide a hierarchical graph structure cooperated with the dilated convolution to capture the scale-specific correlations. A unified neural network is provided to integrate the components above to get the final prediction.
  arXiv  Detail & Related papers  (2022-06-28T08:11:12Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs [65.18780403244178]
  We propose a continuous model to forecast Multivariate Time series with dynamic Graph neural Ordinary Differential Equations (MTGODE) Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures. Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing.
  arXiv  Detail & Related papers  (2022-02-17T02:17:31Z)
• Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting [8.881348323807158]
  We propose a multi-scale adaptive graph neural network (MAGNN) to address the above issue. Experiments on four real-world datasets demonstrate that MAGNN outperforms the state-of-the-art methods across various settings.
  arXiv  Detail & Related papers  (2022-01-13T08:04:10Z)
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [91.65637773358347]
  We propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module. Our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-24T04:02:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.