Cover Image

MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wu, Binqing, Shang, Zongjiang, Huang, Jianlong, Chen, Ling
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ST-Hyper: Learning High-Order Dependencies Across Multiple Spatial-Temporal Scales for Multivariate Time Series Forecasting
by: Wu, Binqing, et al.
Published: (2025)

AirDDE: Multifactor Neural Delay Differential Equations for Air Quality Forecasting
by: Wu, Binqing, et al.
Published: (2026)

MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting
by: Shang, Zongjiang, et al.
Published: (2024)

Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting
by: Shang, Zongjiang, et al.
Published: (2024)

Multi-scale hypergraph meets LLMs: Aligning large language models for time series analysis
by: Shang, Zongjiang, et al.
Published: (2026)

Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators
by: Zhao, Lifan, et al.
Published: (2024)

SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting
by: Xu, Xiongxiao, et al.
Published: (2024)

Multi-period Learning for Financial Time Series Forecasting
by: Zhang, Xu, et al.
Published: (2025)

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
by: Rasul, Kashif, et al.
Published: (2023)

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting
by: Luo, Yingtao, et al.
Published: (2025)

VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting
by: Yang, Yingnan, et al.
Published: (2024)

DMSC: Dynamic Multi-Scale Coordination Framework for Time Series Forecasting
by: Yang, Haonan, et al.
Published: (2025)

MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification
by: Fan, Wei, et al.
Published: (2025)

Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes Learning
by: Bajaj, Chandrajit, et al.
Published: (2024)

Multi-Scale Dilated Convolution Network for Long-Term Time Series Forecasting
by: Li, Feifei, et al.
Published: (2024)

NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining
by: Lin, Chenguo, et al.
Published: (2023)

TSI: A Multi-View Representation Learning Approach for Time Series Forecasting
by: Gao, Wentao, et al.
Published: (2024)

SDGF: Fusing Static and Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting
by: Wang, Shaoxun, et al.
Published: (2025)

TEAFormers: TEnsor-Augmented Transformers for Multi-Dimensional Time Series Forecasting
by: Kong, Linghang, et al.
Published: (2024)

Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models
by: Wang, Hao, et al.
Published: (2025)

SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting
by: Zou, Xiaobei, et al.
Published: (2023)

Causal Time-Series Synchronization for Multi-Dimensional Forecasting
by: Mayr, Michael, et al.
Published: (2024)

Test Time Learning for Time Series Forecasting
by: Christou, Panayiotis, et al.
Published: (2024)

TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts
by: Huang, Yu-Hao, et al.
Published: (2025)

LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting
by: Delibasoglu, Ibrahim, et al.
Published: (2024)

Scaling Law for Time Series Forecasting
by: Shi, Jingzhe, et al.
Published: (2024)

Does Scaling Law Apply in Time Series Forecasting?
by: Li, Zeyan, et al.
Published: (2025)

Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting
by: Wang, Muyao, et al.
Published: (2024)

Numerion: A Multi-Hypercomplex Model for Time Series Forecasting
by: Cao, Hanzhong, et al.
Published: (2025)

ms-Mamba: Multi-scale Mamba for Time-Series Forecasting
by: Karadag, Yusuf Meric, et al.
Published: (2025)

Time-Series Classification for Dynamic Strategies in Multi-Step Forecasting
by: Green, Riku, et al.
Published: (2024)

A Tidal Current Speed Forecasting Model based on Multi-Periodicity Learning
by: Cheng, Tengfei, et al.
Published: (2024)

M$^2$FMoE: Multi-Resolution Multi-View Frequency Mixture-of-Experts for Extreme-Adaptive Time Series Forecasting
by: Huang, Yaohui, et al.
Published: (2026)

DRFormer: Multi-Scale Transformer Utilizing Diverse Receptive Fields for Long Time-Series Forecasting
by: Ding, Ruixin, et al.
Published: (2024)

UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting
by: Liu, Juncheng, et al.
Published: (2024)

LETS Forecast: Learning Embedology for Time Series Forecasting
by: Majeedi, Abrar, et al.
Published: (2025)

Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting
by: Kang, Bong Gyun, et al.
Published: (2024)

When Does Multimodality Lead to Better Time Series Forecasting?
by: Zhang, Xiyuan, et al.
Published: (2025)

LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling
by: Pan, Sheng, et al.
Published: (2026)

MultiCast: Zero-Shot Multivariate Time Series Forecasting Using LLMs
by: Chatzigeorgakidis, Georgios, et al.
Published: (2024)