Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval

• URL: http://arxiv.org/abs/2602.10847v1
• Date: Wed, 11 Feb 2026 13:33:33 GMT
• Title: Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval
• Authors: Fanpu Cao, Lu Dai, Jindong Han, Hui Xiong,
• Abstract summary: We introduce the Global Temporal Retriever (GTR), a lightweight and plug-and-play module designed to extend any forecasting model's temporal awareness beyond the immediate historical context.<n>GTR maintains an adaptive global temporal embedding of the entire cycle and dynamically retrieves relevant global segments with the input sequence.<n>Experiments demonstrate GTR consistently delivers state-of-the-art performance across both short-term and long-term forecasting scenarios.
• Score: 17.278688921668785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multivariate time series forecasting (MTSF) plays a vital role in numerous real-world applications, yet existing models remain constrained by their reliance on a limited historical context. This limitation prevents them from effectively capturing global periodic patterns that often span cycles significantly longer than the input horizon - despite such patterns carrying strong predictive signals. Naive solutions, such as extending the historical window, lead to severe drawbacks, including overfitting, prohibitive computational costs, and redundant information processing. To address these challenges, we introduce the Global Temporal Retriever (GTR), a lightweight and plug-and-play module designed to extend any forecasting model's temporal awareness beyond the immediate historical context. GTR maintains an adaptive global temporal embedding of the entire cycle and dynamically retrieves and aligns relevant global segments with the input sequence. By jointly modeling local and global dependencies through a 2D convolution and residual fusion, GTR effectively bridges short-term observations with long-term periodicity without altering the host model architecture. Extensive experiments on six real-world datasets demonstrate that GTR consistently delivers state-of-the-art performance across both short-term and long-term forecasting scenarios, while incurring minimal parameter and computational overhead. These results highlight GTR as an efficient and general solution for enhancing global periodicity modeling in MTSF tasks. Code is available at this repository: https://github.com/macovaseas/GTR.

Related papers

• MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models [51.506429027626005]
  Memory for Time Series (MEMTS) is a lightweight and plug-and-play method for retrieval-free domain adaptation in time series forecasting.<n>Key component of MEMTS is a Knowledge Persistence Module (KPM), which internalizes domain-specific temporal dynamics.<n>This paradigm shift enables MEMTS to achieve accurate domain adaptation with constant-time inference and near-zero latency.
  arXiv  Detail & Related papers  (2026-02-14T14:00:06Z)
• GEMs: Breaking the Long-Sequence Barrier in Generative Recommendation with a Multi-Stream Decoder [54.64137490632567]
  We propose a novel and unified framework designed to capture users' sequences from long-term history.<n>Generative Multi-streamers ( GEMs) break user sequences into three streams.<n>Extensive experiments on large-scale industrial datasets demonstrate that GEMs significantly outperforms state-the-art methods in recommendation accuracy.
  arXiv  Detail & Related papers  (2026-02-14T06:42:56Z)
• MambaTAD: When State-Space Models Meet Long-Range Temporal Action Detection [94.12444452690329]
  This paper presents MambaTAD, a new state-space TAD model that introduces long-range modeling and global feature detection capabilities.<n>MambaTAD achieves superior TAD performance consistently across multiple public benchmarks.
  arXiv  Detail & Related papers  (2025-11-22T06:04:29Z)
• TIMED: Adversarial and Autoregressive Refinement of Diffusion-Based Time Series Generation [0.31498833540989407]
  TIMED is a unified generative framework that captures global structure via a forward-reverse diffusion process.<n>To further align the real and synthetic distributions in feature space, TIMED incorporates a Maximum Mean Discrepancy (MMD) loss.<n>We show that TIMED generates more realistic and temporally coherent sequences than state-of-the-art generative models.
  arXiv  Detail & Related papers  (2025-09-23T23:05:40Z)
• Interpretable Time Series Autoregression for Periodicity Quantification [18.6300875919604]
  Time series autoregression (AR) is a classical tool for auto-correlations and periodic structures in real-world systems.<n>We revisit this model by introducing sparse autoregression (SAR), where $ell$-norm constraints are used to isolate dominant periodicities.<n>We validate our framework on large-scale mobility and climate time series.
  arXiv  Detail & Related papers  (2025-06-28T14:17:11Z)
• STRGCN: Capturing Asynchronous Spatio-Temporal Dependencies for Irregular Multivariate Time Series Forecasting [14.156419219696252]
  STRGCN captures the complex interdependencies in IMTS by representing them as a fully connected graph.<n>Experiments on four public datasets demonstrate that STRGCN achieves state-of-the-art accuracy, competitive memory usage and training speed.
  arXiv  Detail & Related papers  (2025-05-07T06:41:33Z)
• World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks [53.98633183204453]
  In this paper, a novel world model-based learning framework is proposed to minimize packet-completeness-aware age of information (CAoI) in a vehicular network.<n>A world model framework is proposed to jointly learn a dynamic model of the mmWave V2X environment and use it to imagine trajectories for learning how to perform link scheduling.<n>In particular, the long-term policy is learned in differentiable imagined trajectories instead of environment interactions.
  arXiv  Detail & Related papers  (2025-05-03T06:23:18Z)
• ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models [13.740208247043258]
  We propose ClimateLLM, a foundation model for weather forecasting.<n>It captures temporal dependencies via a cross-temporal and cross-spatial collaborative framework.<n>It integrates frequency decomposition with Large Language Models to strengthen spatial and temporal modeling.
  arXiv  Detail & Related papers  (2025-02-16T09:57:50Z)
• GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing [21.980379175333443]
  We propose a novel Graph Interpolation Attention Recursive Network (named GinAR) to model the spatial-temporal dependencies over the limited collected data for forecasting. In GinAR, it consists of two key components, that is, attention and adaptive graph convolution. Experiments conducted on five real-world datasets demonstrate that GinAR outperforms 11 SOTA baselines, and even when 90% of variables are missing, it can still accurately predict the future values of all variables.
  arXiv  Detail & Related papers  (2024-05-18T16:42:44Z)
• Adapting to Length Shift: FlexiLength Network for Trajectory Prediction [53.637837706712794]
  Trajectory prediction plays an important role in various applications, including autonomous driving, robotics, and scene understanding. Existing approaches mainly focus on developing compact neural networks to increase prediction precision on public datasets, typically employing a standardized input duration. We introduce a general and effective framework, the FlexiLength Network (FLN), to enhance the robustness of existing trajectory prediction against varying observation periods.
  arXiv  Detail & Related papers  (2024-03-31T17:18:57Z)
• Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting [46.63798583414426]
  Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis. Our study demonstrates, through both analytical and empirical evidence, that decomposition is key to containing excessive model inflation. Remarkably, by tailoring decomposition to the intrinsic dynamics of time series data, our proposed model outperforms existing benchmarks.
  arXiv  Detail & Related papers  (2024-01-22T13:15:40Z)
• Grouped self-attention mechanism for a memory-efficient Transformer [64.0125322353281]
  Real-world tasks such as forecasting weather, electricity consumption, and stock market involve predicting data that vary over time. Time-series data are generally recorded over a long period of observation with long sequences owing to their periodic characteristics and long-range dependencies over time. We propose two novel modules, Grouped Self-Attention (GSA) and Compressed Cross-Attention (CCA) Our proposed model efficiently exhibited reduced computational complexity and performance comparable to or better than existing methods.
  arXiv  Detail & Related papers  (2022-10-02T06:58:49Z)

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.