Rethinking Adam for Time Series Forecasting: A Simple Heuristic to Improve Optimization under Distribution Shifts
Fuente:
arXiv
Guardado en:
| Autores principales: | Dong, Yuze, Wu, Jinsong |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
por: Liu, Xvyuan, et al.
Publicado: (2025)
por: Liu, Xvyuan, et al.
Publicado: (2025)
Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution Shifts
por: Qin, Dalin, et al.
Publicado: (2024)
por: Qin, Dalin, et al.
Publicado: (2024)
Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift
por: Sun, Yanru, et al.
Publicado: (2024)
por: Sun, Yanru, et al.
Publicado: (2024)
APT: Affine Prototype-Timestamp For Time Series Forecasting Under Distribution Shift
por: Li, Yujie, et al.
Publicado: (2025)
por: Li, Yujie, et al.
Publicado: (2025)
Human in the Loop Adaptive Optimization for Improved Time Series Forecasting
por: Tiomoko, Malik, et al.
Publicado: (2025)
por: Tiomoko, Malik, et al.
Publicado: (2025)
Simple Contrastive Representation Learning for Time Series Forecasting
por: Zheng, Xiaochen, et al.
Publicado: (2023)
por: Zheng, Xiaochen, et al.
Publicado: (2023)
Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift
por: Chen, Mouxiang, et al.
Publicado: (2023)
por: Chen, Mouxiang, et al.
Publicado: (2023)
The Forecast After the Forecast: A Post-Processing Shift in Time Series
por: Liang, Daojun, et al.
Publicado: (2026)
por: Liang, Daojun, et al.
Publicado: (2026)
Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting
por: Liu, Haoxin, et al.
Publicado: (2026)
por: Liu, Haoxin, et al.
Publicado: (2026)
Simple Feedfoward Neural Networks are Almost All You Need for Time Series Forecasting
por: Sun, Fan-Keng, et al.
Publicado: (2025)
por: Sun, Fan-Keng, et al.
Publicado: (2025)
Causal Time Series Modeling of Supraglacial Lake Evolution in Greenland under Distribution Shift
por: Hossain, Emam, et al.
Publicado: (2025)
por: Hossain, Emam, et al.
Publicado: (2025)
AverageTime: Enhance Long-Term Time Series Forecasting with Simple Averaging
por: Zhao, Gaoxiang, et al.
Publicado: (2024)
por: Zhao, Gaoxiang, et al.
Publicado: (2024)
Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective
por: Wang, Chengsen, et al.
Publicado: (2024)
por: Wang, Chengsen, et al.
Publicado: (2024)
Bias as a Virtue: Rethinking Generalization under Distribution Shifts
por: Chen, Ruixuan, et al.
Publicado: (2025)
por: Chen, Ruixuan, et al.
Publicado: (2025)
Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt
por: Zhang, YiFan, et al.
Publicado: (2024)
por: Zhang, YiFan, et al.
Publicado: (2024)
Expectation Consistency Loss: Rethink Confidence Calibration under Covariate Shift
por: Dong, Jinzong, et al.
Publicado: (2026)
por: Dong, Jinzong, et al.
Publicado: (2026)
ReNF: Rethinking the Design of Neural Long-Term Time Series Forecasters
por: Lu, Yihang, et al.
Publicado: (2025)
por: Lu, Yihang, et al.
Publicado: (2025)
DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting
por: Liu, Zhiding, et al.
Publicado: (2024)
por: Liu, Zhiding, et al.
Publicado: (2024)
Minusformer: Improving Time Series Forecasting by Progressively Learning Residuals
por: Liang, Daojun, et al.
Publicado: (2024)
por: Liang, Daojun, et al.
Publicado: (2024)
Nonstationary Time Series Forecasting via Unknown Distribution Adaptation
por: Li, Zijian, et al.
Publicado: (2024)
por: Li, Zijian, et al.
Publicado: (2024)
LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling
por: Pan, Sheng, et al.
Publicado: (2026)
por: Pan, Sheng, et al.
Publicado: (2026)
Improving Forecasts for Heterogeneous Time Series by "Averaging", with Application to Food Demand Forecast
por: Neubauer, Lukas, et al.
Publicado: (2023)
por: Neubauer, Lukas, et al.
Publicado: (2023)
DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift
por: Wood, Kieran, et al.
Publicado: (2026)
por: Wood, Kieran, et al.
Publicado: (2026)
TSLANet: Rethinking Transformers for Time Series Representation Learning
por: Eldele, Emadeldeen, et al.
Publicado: (2024)
por: Eldele, Emadeldeen, et al.
Publicado: (2024)
ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks
por: Cai, Wanlin, et al.
Publicado: (2024)
por: Cai, Wanlin, et al.
Publicado: (2024)
Metadata Matters for Time Series: Informative Forecasting with Transformers
por: Dong, Jiaxiang, et al.
Publicado: (2024)
por: Dong, Jiaxiang, et al.
Publicado: (2024)
Graphs Generalization under Distribution Shifts
por: Tian, Qin, et al.
Publicado: (2024)
por: Tian, Qin, et al.
Publicado: (2024)
Noise Titration: Exact Distributional Benchmarking for Probabilistic Time Series Forecasting
por: Wang, Qilin
Publicado: (2026)
por: Wang, Qilin
Publicado: (2026)
Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series Representations
por: Grover, Shivam, et al.
Publicado: (2024)
por: Grover, Shivam, et al.
Publicado: (2024)
PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations
por: Qi, Shiyi, et al.
Publicado: (2024)
por: Qi, Shiyi, et al.
Publicado: (2024)
Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators
por: Zhao, Lifan, et al.
Publicado: (2024)
por: Zhao, Lifan, et al.
Publicado: (2024)
Three-Stage Learning Unlocks Strong Performance in Simple Models for Long-Term Time Series Forecasting
por: Yu, Zhenan, et al.
Publicado: (2026)
por: Yu, Zhenan, et al.
Publicado: (2026)
Rethinking Recurrent Neural Networks for Time Series Forecasting: A Reinforced Recurrent Encoder with Prediction-Oriented Proximal Policy Optimization
por: Lai, Xin, et al.
Publicado: (2026)
por: Lai, Xin, et al.
Publicado: (2026)
Revisiting Attention for Multivariate Time Series Forecasting
por: Wu, Haixiang
Publicado: (2024)
por: Wu, Haixiang
Publicado: (2024)
S2TX: Cross-Attention Multi-Scale State-Space Transformer for Time Series Forecasting
por: Wu, Zihao, et al.
Publicado: (2025)
por: Wu, Zihao, et al.
Publicado: (2025)
AROpt: An Optimization Method for Autoregressive Time Series Forecasting
por: Li, Zheng, et al.
Publicado: (2026)
por: Li, Zheng, et al.
Publicado: (2026)
Fully Automated Correlated Time Series Forecasting in Minutes
por: Wu, Xinle, et al.
Publicado: (2024)
por: Wu, Xinle, et al.
Publicado: (2024)
HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting
por: Wang, Tan, et al.
Publicado: (2025)
por: Wang, Tan, et al.
Publicado: (2025)
A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models
por: Xu, Jingjing, et al.
Publicado: (2025)
por: Xu, Jingjing, et al.
Publicado: (2025)
Rethinking Time Series Forecasting with LLMs via Nearest Neighbor Contrastive Learning
por: Bogahawatte, Jayanie, et al.
Publicado: (2024)
por: Bogahawatte, Jayanie, et al.
Publicado: (2024)
Ejemplares similares
-
Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
por: Liu, Xvyuan, et al.
Publicado: (2025) -
Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution Shifts
por: Qin, Dalin, et al.
Publicado: (2024) -
Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift
por: Sun, Yanru, et al.
Publicado: (2024) -
APT: Affine Prototype-Timestamp For Time Series Forecasting Under Distribution Shift
por: Li, Yujie, et al.
Publicado: (2025) -
Human in the Loop Adaptive Optimization for Improved Time Series Forecasting
por: Tiomoko, Malik, et al.
Publicado: (2025)