Imagen de Portada

Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Wang, Chengsen, Qi, Qi, Wang, Jingyu, Sun, Haifeng, Zhuang, Zirui, Wu, Jinming, Liao, Jianxin
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Unlocking the Potential of Linear Networks for Irregular Multivariate Time Series Forecasting
por: Wang, Chengsen, et al.
Publicado: (2025)

ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data
por: Wang, Chengsen, et al.
Publicado: (2024)

Interdependency Matters: Graph Alignment for Multivariate Time Series Anomaly Detection
por: Wang, Yuanyi, et al.
Publicado: (2024)

OIPR: Evaluation for Time-series Anomaly Detection Inspired by Operator Interest
por: Jing, Yuhan, et al.
Publicado: (2025)

ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Data
por: Wang, Chengsen, et al.
Publicado: (2025)

Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation
por: Wang, Yuanyi, et al.
Publicado: (2024)

MergeQuant: Accurate 4-bit Static Quantization of Large Language Models by Channel-wise Calibration
por: Wang, Jinguang, et al.
Publicado: (2025)

APT: Affine Prototype-Timestamp For Time Series Forecasting Under Distribution Shift
por: Li, Yujie, et al.
Publicado: (2025)

IndexNet: Timestamp and Variable-Aware Modeling for Time Series Forecasting
por: Wu, Beiliang, et al.
Publicado: (2025)

PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations
por: Qi, Shiyi, et al.
Publicado: (2024)

LiNo: Advancing Recursive Residual Decomposition of Linear and Nonlinear Patterns for Robust Time Series Forecasting
por: Yu, Guoqi, et al.
Publicado: (2024)

HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting
por: Wang, Tan, et al.
Publicado: (2025)

StaTS: Spectral Trajectory Schedule Learning for Adaptive Time Series Forecasting with Frequency Guided Denoiser
por: Zhang, Jintao, et al.
Publicado: (2026)

Erasing Without Remembering: Implicit Knowledge Forgetting in Large Language Models
por: Wang, Huazheng, et al.
Publicado: (2025)

DIVER: A Robust Text-to-SQL System with Dynamic Interactive Value Linking and Evidence Reasoning
por: Nan, Yafeng, et al.
Publicado: (2026)

TimeGPT in Load Forecasting: A Large Time Series Model Perspective
por: Liao, Wenlong, et al.
Publicado: (2024)

Attention as Robust Representation for Time Series Forecasting
por: Niu, PeiSong, et al.
Publicado: (2024)

Selective Learning for Deep Time Series Forecasting
por: Fu, Yisong, et al.
Publicado: (2025)

Dual-Forecaster: A Multimodal Time Series Model Integrating Descriptive and Predictive Texts
por: Wu, Wenfa, et al.
Publicado: (2025)

WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting
por: Liu, Peiyuan, et al.
Publicado: (2023)

Unlocking the Power of LSTM for Long Term Time Series Forecasting
por: Kong, Yaxuan, et al.
Publicado: (2024)

LogoRA: Local-Global Representation Alignment for Robust Time Series Classification
por: Zhang, Huanyu, et al.
Publicado: (2024)

Aurora: Towards Universal Generative Multimodal Time Series Forecasting
por: Wu, Xingjian, et al.
Publicado: (2025)

GLU: Global-Local-Uncertainty Fusion for Scalable Spatiotemporal Reconstruction and Forecasting
por: Wang, Linzheng, et al.
Publicado: (2026)

The Forecast After the Forecast: A Post-Processing Shift in Time Series
por: Liang, Daojun, et al.
Publicado: (2026)

OutlierTune: Efficient Channel-Wise Quantization for Large Language Models
por: Wang, Jinguang, et al.
Publicado: (2024)

Robust Multivariate Time Series Forecasting against Intra- and Inter-Series Transitional Shift
por: He, Hui, et al.
Publicado: (2024)

Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting
por: Liu, Haoxin, et al.
Publicado: (2026)

Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
por: Liu, Xvyuan, et al.
Publicado: (2025)

Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching
por: Xu, Huibo, et al.
Publicado: (2025)

RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies
por: Cheng, Hao, et al.
Publicado: (2024)

SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion
por: Han, Lu, et al.
Publicado: (2024)

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

Logo-LLM: Local and Global Modeling with Large Language Models for Time Series Forecasting
por: Ou, Wenjie, et al.
Publicado: (2025)

Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecasting
por: Cheng, Mingyue, et al.
Publicado: (2025)

Towards Stable and Structured Time Series Generation with Perturbation-Aware Flow Matching
por: Zhang, Jintao, et al.
Publicado: (2025)

TimeSAF: Towards LLM-Guided Semantic Asynchronous Fusion for Time Series Forecasting
por: Zhang, Fan, et al.
Publicado: (2026)

CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting
por: Liu, Yaguo, et al.
Publicado: (2026)

The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting
por: Shen, Lefei, et al.
Publicado: (2025)

SEMPO: Lightweight Foundation Models for Time Series Forecasting
por: He, Hui, et al.
Publicado: (2025)