Let Experts Feel Uncertainty: A Multi-Expert Label Distribution Approach to Probabilistic Time Series Forecasting
Fuente:
arXiv
Guardado en:
| Autores principales: | Zhou, Zhen, Wang, Zhirui, Hong, Qi, Shi, Yunyang, Gu, Ziyuan, Liu, Zhiyuan |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Wavelet Mixture of Experts for Time Series Forecasting
por: Zhou, Zheng, et al.
Publicado: (2025)
por: Zhou, Zheng, et al.
Publicado: (2025)
Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration
por: Wen, Haonan, et al.
Publicado: (2026)
por: Wen, Haonan, et al.
Publicado: (2026)
SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting
por: Xu, Xiongxiao, et al.
Publicado: (2024)
por: Xu, Xiongxiao, et al.
Publicado: (2024)
DDT: A Dual-Masking Dual-Expert Transformer for Energy Time-Series Forecasting
por: Zhu, Mingnan, et al.
Publicado: (2026)
por: Zhu, Mingnan, et al.
Publicado: (2026)
AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting
por: Wang, Rui, et al.
Publicado: (2026)
por: Wang, Rui, et al.
Publicado: (2026)
Mixture-of-Linear-Experts for Long-term Time Series Forecasting
por: Ni, Ronghao, et al.
Publicado: (2023)
por: Ni, Ronghao, et al.
Publicado: (2023)
MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models
por: Liu, Yiwen, et al.
Publicado: (2025)
por: Liu, Yiwen, et al.
Publicado: (2025)
Beyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecasting
por: Wang, Yijun, et al.
Publicado: (2026)
por: Wang, Yijun, et al.
Publicado: (2026)
MTmixAtt: Integrating Mixture-of-Experts with Multi-Mix Attention for Large-Scale Recommendation
por: Qi, Xianyang, et al.
Publicado: (2025)
por: Qi, Xianyang, et al.
Publicado: (2025)
MoHETS: Long-term Time Series Forecasting with Mixture-of-Heterogeneous-Experts
por: Ortigossa, Evandro S., et al.
Publicado: (2026)
por: Ortigossa, Evandro S., et al.
Publicado: (2026)
Seg-MoE: Multi-Resolution Segment-wise Mixture-of-Experts for Time Series Forecasting Transformers
por: Ortigossa, Evandro S., et al.
Publicado: (2026)
por: Ortigossa, Evandro S., et al.
Publicado: (2026)
Klotski: Efficient Mixture-of-Expert Inference via Expert-Aware Multi-Batch Pipeline
por: Fang, Zhiyuan, et al.
Publicado: (2025)
por: Fang, Zhiyuan, et al.
Publicado: (2025)
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
por: Shi, Xiaoming, et al.
Publicado: (2024)
por: Shi, Xiaoming, et al.
Publicado: (2024)
A Time Series is Worth Five Experts: Heterogeneous Mixture of Experts for Traffic Flow Prediction
por: Wang, Guangyu, et al.
Publicado: (2024)
por: Wang, Guangyu, et al.
Publicado: (2024)
Performative Time-Series Forecasting
por: Zhao, Zhiyuan, et al.
Publicado: (2023)
por: Zhao, Zhiyuan, et al.
Publicado: (2023)
M$^2$FMoE: Multi-Resolution Multi-View Frequency Mixture-of-Experts for Extreme-Adaptive Time Series Forecasting
por: Huang, Yaohui, et al.
Publicado: (2026)
por: Huang, Yaohui, et al.
Publicado: (2026)
TRIP-Evaluate: An Open Multimodal Benchmark for Evaluating Large Models in Transportation
por: Gong, Han, et al.
Publicado: (2026)
por: Gong, Han, et al.
Publicado: (2026)
Multi-Modal Time Series Prediction via Mixture of Modulated Experts
por: Zhang, Lige, et al.
Publicado: (2026)
por: Zhang, Lige, et al.
Publicado: (2026)
MoDEx: Mixture of Depth-specific Experts for Multivariate Long-term Time Series Forecasting
por: Yoon, Hyekyung, et al.
Publicado: (2026)
por: Yoon, Hyekyung, et al.
Publicado: (2026)
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting
por: Zhang, Lingzheng, et al.
Publicado: (2024)
por: Zhang, Lingzheng, et al.
Publicado: (2024)
Deep Clustering Survival Machines with Interpretable Expert Distributions
por: Hou, Bojian, et al.
Publicado: (2023)
por: Hou, Bojian, et al.
Publicado: (2023)
Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecasting
por: Zhu, Jiawen, et al.
Publicado: (2026)
por: Zhu, Jiawen, et al.
Publicado: (2026)
WaveMoE: A Wavelet-Enhanced Mixture-of-Experts Foundation Model for Time Series Forecasting
por: Wu, Shunyu, et al.
Publicado: (2026)
por: Wu, Shunyu, et al.
Publicado: (2026)
MoDE: A Mixture-of-Experts Model with Mutual Distillation among the Experts
por: Xie, Zhitian, et al.
Publicado: (2024)
por: Xie, Zhitian, et al.
Publicado: (2024)
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
por: Liu, Haoxin, et al.
Publicado: (2024)
por: Liu, Haoxin, et al.
Publicado: (2024)
Non-stationary Diffusion For Probabilistic Time Series Forecasting
por: Ye, Weiwei, et al.
Publicado: (2025)
por: Ye, Weiwei, et al.
Publicado: (2025)
Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
por: Zhou, Yitong, et al.
Publicado: (2025)
por: Zhou, Yitong, et al.
Publicado: (2025)
Ada-MoGE: Adaptive Mixture of Gaussian Expert Model for Time Series Forecasting
por: Ni, Zhenliang, et al.
Publicado: (2025)
por: Ni, Zhenliang, et al.
Publicado: (2025)
TradExpert: Revolutionizing Trading with Mixture of Expert LLMs
por: Ding, Qianggang, et al.
Publicado: (2024)
por: Ding, Qianggang, et al.
Publicado: (2024)
Counterfactual Probabilistic Diffusion with Expert Models
por: Mu, Wenhao, et al.
Publicado: (2025)
por: Mu, Wenhao, et al.
Publicado: (2025)
Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers
por: Kang, Hyeongwon, et al.
Publicado: (2026)
por: Kang, Hyeongwon, et al.
Publicado: (2026)
Scalable Heterogeneous Graph Learning via Heterogeneous-aware Orthogonal Prototype Experts
por: Zhou, Wei, et al.
Publicado: (2026)
por: Zhou, Wei, et al.
Publicado: (2026)
A Stock Price Prediction Approach Based on Time Series Decomposition and Multi-Scale CNN using OHLCT Images
por: Pei, Zhiyuan, et al.
Publicado: (2024)
por: Pei, Zhiyuan, et al.
Publicado: (2024)
Mastering the Minority: An Uncertainty-guided Multi-Expert Framework for Challenging-tailed Sequence Learning
por: Wang, Ye, et al.
Publicado: (2026)
por: Wang, Ye, et al.
Publicado: (2026)
Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
por: Fan, Wei, et al.
Publicado: (2024)
por: Fan, Wei, et al.
Publicado: (2024)
Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models
por: Wang, Zihan, et al.
Publicado: (2024)
por: Wang, Zihan, et al.
Publicado: (2024)
Beyond Redundancy: Diverse and Specialized Multi-Expert Sparse Autoencoder
por: Xu, Zhen, et al.
Publicado: (2025)
por: Xu, Zhen, et al.
Publicado: (2025)
Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
por: Cortés, Adrien, et al.
Publicado: (2025)
por: Cortés, Adrien, et al.
Publicado: (2025)
Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
por: Lu, Xudong, et al.
Publicado: (2024)
por: Lu, Xudong, et al.
Publicado: (2024)
Compass: Navigating Global Marine Lead Data Integration through Expert-Guided LLM Agent
por: Liu, Yiming, et al.
Publicado: (2026)
por: Liu, Yiming, et al.
Publicado: (2026)
Ejemplares similares
-
Wavelet Mixture of Experts for Time Series Forecasting
por: Zhou, Zheng, et al.
Publicado: (2025) -
Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration
por: Wen, Haonan, et al.
Publicado: (2026) -
SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting
por: Xu, Xiongxiao, et al.
Publicado: (2024) -
DDT: A Dual-Masking Dual-Expert Transformer for Energy Time-Series Forecasting
por: Zhu, Mingnan, et al.
Publicado: (2026) -
AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting
por: Wang, Rui, et al.
Publicado: (2026)