Image de couverture de livre

Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Xiong, Zhangzhi, Wu, Haoyi, Wu, You, Gu, Shuqi, Ren, Kan, Tu, Kewei
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

ConTSG-Bench: A Unified Benchmark for Conditional Time Series Generation
par: Lan, Shaocheng, et autres
Publié: (2026)

ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
par: Chen, Yuqi, et autres
Publié: (2024)

Probabilistic Learning of Multivariate Time Series with Temporal Irregularity
par: Li, Yijun, et autres
Publié: (2023)

Transformers and Their Roles as Time Series Foundation Models
par: Wu, Dennis, et autres
Publié: (2025)

ST-MTM: Masked Time Series Modeling with Seasonal-Trend Decomposition for Time Series Forecasting
par: Seo, Hyunwoo, et autres
Publié: (2025)

ST-Tree with Interpretability for Multivariate Time Series Classification
par: Du, Mingsen, et autres
Publié: (2024)

Leveraging 2D Information for Long-term Time Series Forecasting with Vanilla Transformers
par: Cheng, Xin, et autres
Publié: (2024)

Toto: Time Series Optimized Transformer for Observability
par: Cohen, Ben, et autres
Publié: (2024)

ST-Hyper: Learning High-Order Dependencies Across Multiple Spatial-Temporal Scales for Multivariate Time Series Forecasting
par: Wu, Binqing, et autres
Publié: (2025)

Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting
par: Li, Jinglin, et autres
Publié: (2026)

SSD-TS: Exploring the Potential of Linear State Space Models for Diffusion Models in Time Series Imputation
par: Gao, Hongfan, et autres
Publié: (2024)

SageFormer: Series-Aware Framework for Long-term Multivariate Time Series Forecasting
par: Zhang, Zhenwei, et autres
Publié: (2023)

Unified Training of Universal Time Series Forecasting Transformers
par: Woo, Gerald, et autres
Publié: (2024)

Context-Aware Probabilistic Modeling with LLM for Multimodal Time Series Forecasting
par: Yao, Yueyang, et autres
Publié: (2025)

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
par: Rasul, Kashif, et autres
Publié: (2023)

A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models
par: Xu, Jingjing, et autres
Publié: (2025)

Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classification
par: Kan, Ziwen, et autres
Publié: (2024)

HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting
par: Feng, Shibo, et autres
Publié: (2025)

Scaling Probabilistic Transformer via Efficient Cross-Scale Hyperparameter Transfer
par: Kuang, Penghao, et autres
Publié: (2026)

Non-stationary Diffusion For Probabilistic Time Series Forecasting
par: Ye, Weiwei, et autres
Publié: (2025)

Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation
par: Dobhal, Umang, et autres
Publié: (2026)

Stochastic Diffusion: A Diffusion Probabilistic Model for Stochastic Time Series Forecasting
par: Liu, Yuansan, et autres
Publié: (2024)

RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting
par: Lai, Chih-Yu, et autres
Publié: (2025)

TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
par: Wang, Yuxuan, et autres
Publié: (2024)

LENS: Large Pre-trained Transformer for Exploring Financial Time Series Regularities
par: Xu, Yuanjian, et autres
Publié: (2024)

KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning
par: Feng, Kun, et autres
Publié: (2026)

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
par: Wu, Xingjian, et autres
Publié: (2025)

Cisco Time Series Model Technical Report
par: Gou, Liang, et autres
Publié: (2025)

DE3S: Dual-Enhanced Soft-Sparse-Shape Learning for Medical Early Time-Series Classification
par: Xie, Tao, et autres
Publié: (2025)

Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting
par: Yang, Runze, et autres
Publié: (2025)

Let Experts Feel Uncertainty: A Multi-Expert Label Distribution Approach to Probabilistic Time Series Forecasting
par: Zhou, Zhen, et autres
Publié: (2026)

Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models
par: Zhao, Yida, et autres
Publié: (2024)

Revisiting Attention for Multivariate Time Series Forecasting
par: Wu, Haixiang
Publié: (2024)

Latent Space Score-based Diffusion Model for Probabilistic Multivariate Time Series Imputation
par: Liang, Guojun, et autres
Publié: (2024)

Beyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecasting
par: Wang, Yijun, et autres
Publié: (2026)

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
par: Cortés, Adrien, et autres
Publié: (2025)

Synthetic Series-Symbol Data Generation for Time Series Foundation Models
par: Wang, Wenxuan, et autres
Publié: (2025)

pyFAST: A Modular PyTorch Framework for Time Series Modeling with Multi-source and Sparse Data
par: Wang, Zhijin, et autres
Publié: (2025)

Don't Retrain, Align: Adapting Autoregressive LMs to Diffusion LMs via Representation Alignment
par: Peng, Fred Zhangzhi, et autres
Publié: (2026)

Rough Transformers for Continuous and Efficient Time-Series Modelling
par: Moreno-Pino, Fernando, et autres
Publié: (2024)