Imagen de Portada

Baguan-TS: A Sequence-Native In-Context Learning Model for Time Series Forecasting with Covariates

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Yang, Linxiao, Jiang, Xue, Xu, Gezheng, Zhou, Tian, Yang, Min, Zhu, ZhaoYang, Geng, Linyuan, Zeng, Zhipeng, Chen, Qiming, Gu, Xinyue, Jin, Rong, Sun, Liang
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

SolarBoost: Distributed Photovoltaic Power Forecasting Amid Time-varying Grid Capacity
por: Geng, Linyuan, et al.
Publicado: (2025)

ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting
por: Peng, Ziheng, et al.
Publicado: (2025)

LightGTS-Cov: Covariate-Enhanced Time Series Forecasting
por: Shang, Yong, et al.
Publicado: (2026)

TriForecaster: A Mixture of Experts Framework for Multi-Region Electric Load Forecasting with Tri-dimensional Specialization
por: Zhu, Zhaoyang, et al.
Publicado: (2025)

Utilizing Strategic Pre-training to Reduce Overfitting: Baguan -- A Pre-trained Weather Forecasting Model
por: Niu, Peisong, et al.
Publicado: (2025)

CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models
por: Wang, Xiaorui, et al.
Publicado: (2026)

AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting
por: Wang, Rui, et al.
Publicado: (2026)

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

SLIM: a Scalable Light-weight Root Cause Analysis for Imbalanced Data in Microservice
por: Ren, Rui, et al.
Publicado: (2024)

Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter View
por: Niu, Peisong, et al.
Publicado: (2023)

Zero-Shot Time Series Forecasting with Covariates via In-Context Learning
por: Auer, Andreas, et al.
Publicado: (2025)

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

DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting
por: Liu, Zhiding, et al.
Publicado: (2024)

Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
por: Hu, Yifan, et al.
Publicado: (2025)

CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting
por: Xue, Wang, et al.
Publicado: (2023)

DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters
por: Zhang, Haoran, et al.
Publicado: (2026)

AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification
por: Chen, Yuxuan, et al.
Publicado: (2025)

FiCoTS: Fine-to-Coarse LLM-Enhanced Hierarchical Cross-Modality Interaction for Time Series Forecasting
por: Lyu, Yafei, et al.
Publicado: (2025)

Nonstationary Functional Time Series Forecasting
por: Han Lin Shang, et al.
Publicado: (2024)

Semantically-Guided Inference for Conditional Diffusion Models: Enhancing Covariate Consistency in Time Series Forecasting
por: Ding, Rui, et al.
Publicado: (2025)

Task-oriented Time Series Imputation Evaluation via Generalized Representers
por: Wang, Zhixian, et al.
Publicado: (2024)

Integrated Influence: Data Attribution with Baseline
por: Yang, Linxiao, et al.
Publicado: (2025)

TriTS: Time Series Forecasting from a Multimodal Perspective
por: Ao, Xiang
Publicado: (2026)

FilterTS: Comprehensive Frequency Filtering for Multivariate Time Series Forecasting
por: Wang, Yulong, et al.
Publicado: (2025)

CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect
por: Zhou, Jiehui, et al.
Publicado: (2024)

QuAnTS: Question Answering on Time Series
por: Divo, Felix, et al.
Publicado: (2025)

CITRAS: Covariate-Informed Transformer for Time Series Forecasting
por: Yamaguchi, Yosuke, et al.
Publicado: (2025)

FLAIRR-TS -- Forecasting LLM-Agents with Iterative Refinement and Retrieval for Time Series
por: Jalori, Gunjan, et al.
Publicado: (2025)

Fidel-TS: A High-Fidelity Multimodal Benchmark for Time Series Forecasting
por: Xu, Zhijian, et al.
Publicado: (2025)

Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting
por: Yang, Haonan, et al.
Publicado: (2026)

TimeExpert: Boosting Long Time Series Forecasting with Temporal Mix of Experts
por: Ma, Xiaowen, et al.
Publicado: (2025)

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

Context-Sensitive Pointer Analysis for ArkTS
por: Yang, Yizhuo, et al.
Publicado: (2026)

FlowTS: Time Series Generation via Rectified Flow
por: Hu, Yang, et al.
Publicado: (2024)

Distilling Time Series Foundation Models for Efficient Forecasting
por: Li, Yuqi, et al.
Publicado: (2026)

TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting
por: Feng, Shibo, et al.
Publicado: (2025)

TS2Vec-Ensemble: An Enhanced Self-Supervised Framework for Time Series Forecasting
por: Niroshan, Ganeshan, et al.
Publicado: (2025)

NuwaTS: a Foundation Model Mending Every Incomplete Time Series
por: Cheng, Jinguo, et al.
Publicado: (2024)

EasyTime: Time Series Forecasting Made Easy
por: Qiu, Xiangfei, et al.
Publicado: (2024)

HyperbolicRAG: Enhancing Retrieval-Augmented Generation with Hyperbolic Representations
por: Cao, Linxiao, et al.
Publicado: (2025)