TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching

• URL: http://arxiv.org/abs/2606.03121v1
• Date: Tue, 02 Jun 2026 04:06:12 GMT
• Title: TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching
• Abstract summary: TiWeaver is a unified framework designed to handle temporal dynamics and fine-grained inter-channel dependencies adaptively.<n>We evaluate TiWeaver on 12 real-world time series datasets, where it achieves state-of-the-art performance.
• Score: 15.279995924017458
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multivariate time series forecasting plays a critical role in real-world applications, including weather prediction, stock analysis, and health monitoring. Due to the diversity of data sources, time series exhibit diverse temporal dynamics, often accompanied by various irregularities such as missing values and non-uniform sampling frequencies. Such irregularities lead to complex and asynchronous temporal dependencies across channels. Thus, a single model with a fixed patching scheme often fails to adapt well to diverse multivariate time series, hindering accurate forecasting. In this paper, we propose TiWeaver, a unified framework designed to handle temporal dynamics and fine-grained inter-channel dependencies adaptively. Specifically, we introduce a Graph-Guided Adaptive Tokenizer (G$^2$AT) that divides time series into high contextually coherent patches by jointly considering temporal density and representation consistency. In addition, we propose a Fine-grained Asynchronous Dependency Extractor (FADE), which is designed to model fine-grained asynchronous inter-channel dependencies while incorporating long-term historical dependencies. We evaluate TiWeaver on 12 real-world time series datasets, where it achieves state-of-the-art performance, outperforming existing methods up to 25%. These results demonstrate its robustness and effectiveness across diverse domains and data characteristics.

Related papers

• 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)
• UniDiff: A Unified Diffusion Framework for Multimodal Time Series Forecasting [90.47915032778366]
  We propose UniDiff, a unified diffusion framework for multimodal time series forecasting.<n>At its core lies a unified and parallel fusion module, where a single cross-attention mechanism integrates structural information from timestamps and semantic context from texts.<n>Experiments on real-world benchmark datasets across eight domains demonstrate that the proposed UniDiff model achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-12-08T05:36:14Z)
• 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)
• SDGF: Fusing Static and Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting [9.027814258970684]
  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.
  arXiv  Detail & Related papers  (2025-09-14T11:23:12Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [42.60778405812048]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• Time Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines [5.543238821368548]
  Time series often exhibit significant diversity in their temporal patterns across different time spans and domains.<n>Time Tracker achieves state-of-the-art performance in predicting accuracy, model generalization and adaptability.
  arXiv  Detail & Related papers  (2025-05-21T06:18:41Z)
• Unlocking the Potential of Linear Networks for Irregular Multivariate Time Series Forecasting [24.802227163932322]
  Time series forecasting holds significant importance across various industries, including finance, transportation, energy, healthcare, and climate.<n>This paper introduces an adaptive linear network capable of dynamically adjusting weights according to observation time points to address intra-series inconsistency.<n> Comprehensive experiments across four benchmark datasets demonstrate the superiority of AiT, improving prediction accuracy by 11% and decreasing runtime by 52% compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-05-01T15:24:48Z)
• 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)
• 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)
• Instance-wise Graph-based Framework for Multivariate Time Series Forecasting [69.38716332931986]
  We propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps. The key idea of our framework is aggregating information from the historical time series of different variables to the current time series that we need to forecast.
  arXiv  Detail & Related papers  (2021-09-14T07:38:35Z)
• Multivariate Time-series Anomaly Detection via Graph Attention Network [27.12694738711663]
  Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications. One major limitation is that they do not capture the relationships between different time-series explicitly. We propose a novel self-supervised framework for multivariate time-series anomaly detection to address this issue.
  arXiv  Detail & Related papers  (2020-09-04T07:46:19Z)
• 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.