Imagen de Portada

Deep Time-series Forecasting Needs Kernelized Moment Balancing

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Pan, Licheng, Wang, Hao, Yang, Haocheng, Li, Yuqi, Wen, Qingsong, Li, Xiaoxi, Chen, Zhichao, Li, Haoxuan, Chu, Zhixuan, Lu, Yuan
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

DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment
por: Wang, Hao, et al.
Publicado: (2025)

Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models
por: Wang, Hao, et al.
Publicado: (2025)

Deep Autocorrelation Modeling for Time-Series Forecasting: Progress and Prospects
por: Wang, Hao, et al.
Publicado: (2026)

Time-o1: Time-Series Forecasting Needs Transformed Label Alignment
por: Wang, Hao, et al.
Publicado: (2025)

CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks
por: Wang, Hao, et al.
Publicado: (2026)

ImplicitRM: Unbiased Reward Modeling from Implicit Preference Data for LLM alignment
por: Wang, Hao, et al.
Publicado: (2026)

Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting
por: Pan, Licheng, et al.
Publicado: (2025)

FreDF: Learning to Forecast in the Frequency Domain
por: Wang, Hao, et al.
Publicado: (2024)

Robust Reward Modeling for Large Language Models via Causal Decomposition
por: Lu, Yunsheng, et al.
Publicado: (2026)

Analyzing and Improving Diffusion Models for Time-Series Data Imputation: A Proximal Recursion Perspective
por: Chen, Zhichao, et al.
Publicado: (2026)

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
por: Jin, Ming, et al.
Publicado: (2023)

Optimal Transport for LLM Reward Modeling from Noisy Preference
por: Pan, Licheng, et al.
Publicado: (2026)

BEAT: Balanced Frequency Adaptive Tuning for Long-Term Time-Series Forecasting
por: Li, Zhixuan, et al.
Publicado: (2025)

Proximity Matters: Local Proximity Enhanced Balancing for Treatment Effect Estimation
por: Wang, Hao, et al.
Publicado: (2024)

From Text to Talk: Audio-Language Model Needs Non-Autoregressive Joint Training
por: Liu, Tianqiao, et al.
Publicado: (2025)

DDTime: Dataset Distillation with Spectral Alignment and Information Bottleneck for Time-Series Forecasting
por: Li, Yuqi, et al.
Publicado: (2025)

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

Debiased Collaborative Filtering with Kernel-Based Causal Balancing
por: Li, Haoxuan, et al.
Publicado: (2024)

Beyond Extrapolation: Knowledge Utilization Paradigm with Bidirectional Inspiration for Time Series Forecasting
por: Chong, Liu, et al.
Publicado: (2026)

Non-Stationary Time Series Forecasting Based on Fourier Analysis and Cross Attention Mechanism
por: Xiong, Yuqi, et al.
Publicado: (2025)

TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecasting
por: Hu, Jiaxi, et al.
Publicado: (2024)

Multi-Order Wavelet Derivative Transform for Deep Time Series Forecasting
por: Zhou, Ziyu, 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)

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting
por: Zhong, Siru, et al.
Publicado: (2025)

DeepFilter: A Transformer-style Framework for Accurate and Efficient Process Monitoring
por: Wang, Hao, et al.
Publicado: (2025)

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

SeesawNet: Towards Non-stationary Time Series Forecasting with Balanced Modeling of Common and Specific Dependencies
por: Li, Hao, et al.
Publicado: (2026)

Causal Interventional Prediction System for Robust and Explainable Effect Forecasting
por: Chu, Zhixuan, et al.
Publicado: (2024)

FPN-fusion: Enhanced Linear Complexity Time Series Forecasting Model
por: Li, Chu, et al.
Publicado: (2024)

Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting
por: Zhan, Tianxiang, et al.
Publicado: (2024)

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

Evolution of Kernels: Automated RISC-V Kernel Optimization with Large Language Models
por: Chen, Siyuan, et al.
Publicado: (2025)

Understanding and Mitigating Overrefusal in LLMs from an Unveiling Perspective of Safety Decision Boundary
por: Pan, Licheng, et al.
Publicado: (2025)

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting
por: Zhong, Siru, et al.
Publicado: (2026)

Entire Space Counterfactual Learning for Reliable Content Recommendations
por: Wang, Hao, et al.
Publicado: (2022)

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
por: Shi, Xiaoming, et al.
Publicado: (2024)

Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
por: Hu, Jiaxi, et al.
Publicado: (2024)

Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting
por: Jin, Ming, et al.
Publicado: (2023)

Time-dependent Regularized 13-Moment Equations with Onsager Boundary Conditions in the Linear Regime
por: Lin, Bo, et al.
Publicado: (2024)

HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting
por: Zhao, Shubao, et al.
Publicado: (2024)