Image de couverture de livre

Forecast-Then-Optimize Deep Learning Methods

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Jiang, Jinhang, Wu, Nan, Liu, Ben, Feng, Mei, Ji, Xin, Srinivasan, Karthik
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Deep Transfer $Q$-Learning for Offline Non-Stationary Reinforcement Learning
par: Chai, Jinhang, et autres
Publié: (2025)

Structured Matrix Learning under Arbitrary Entrywise Dependence and Estimation of Markov Transition Kernel
par: Chai, Jinhang, et autres
Publié: (2024)

Transition Transfer $Q$-Learning for Composite Markov Decision Processes
par: Chai, Jinhang, et autres
Publié: (2025)

Low-Rank Plus Sparse Matrix Transfer Learning under Growing Representations and Ambient Dimensions
par: Chai, Jinhang, et autres
Publié: (2026)

Predictive Crash Analytics for Traffic Safety using Deep Learning
par: Sivakoti, Karthik
Publié: (2025)

Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization
par: Guo, Yizhen, et autres
Publié: (2024)

DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection
par: Mei, Lin-Feng, et autres
Publié: (2024)

Deep Learning for Time Series Forecasting: A Survey
par: Kong, Xiangjie, et autres
Publié: (2025)

Explore the Ideology of Deep Learning in ENSO Forecasts
par: Gan, Yanhai, et autres
Publié: (2026)

Forecasting Day-Ahead Electricity Prices in the Integrated Single Electricity Market: Addressing Volatility with Comparative Machine Learning Methods
par: Harkin, Ben, et autres
Publié: (2024)

Deep k-grouping: An Unsupervised Learning Framework for Combinatorial Optimization on Graphs and Hypergraphs
par: Bai, Sen, et autres
Publié: (2025)

Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
par: Wang, Rushan, et autres
Publié: (2024)

LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting
par: Zhuang, Yilin, et autres
Publié: (2025)

Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learning
par: Zhang, Hai, et autres
Publié: (2024)

Forecasting Drought Using Machine Learning in California
par: Li, Nan K., et autres
Publié: (2025)

Forecasting Smog Clouds With Deep Learning
par: Oldenburg, Valentijn, et autres
Publié: (2024)

DGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting
par: Liu, Qinshuo, et autres
Publié: (2024)

Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models
par: Meng, Shuchen, et autres
Publié: (2024)

A Self-Attentive Meta-Optimizer with Group-Adaptive Learning Rates and Weight Decay
par: Zhao, JiangBo, et autres
Publié: (2026)

A Comparative Study of Traditional Machine Learning, Deep Learning, and Large Language Models for Mental Health Forecasting using Smartphone Sensing Data
par: Feng, Kaidong, et autres
Publié: (2026)

An Efficient Plugin Method for Metric Optimization of Black-Box Models
par: Devic, Siddartha, et autres
Publié: (2025)

Reducing Bias in Deep Learning Optimization: The RSGDM Approach
par: Qin, Honglin, et autres
Publié: (2024)

A Survey on Uncertainty Quantification Methods for Deep Learning
par: He, Wenchong, et autres
Publié: (2023)

Selective Learning for Deep Time Series Forecasting
par: Fu, Yisong, et autres
Publié: (2025)

Hybrid Deep Learning Approach for Coupled Demand Forecasting and Supply Chain Optimization
par: Nadia, Nusrat Yasmin, et autres
Publié: (2026)

Forecasting VIX using Bayesian Deep Learning
par: Hortúa, Héctor J., et autres
Publié: (2024)

Online Data Augmentation for Forecasting with Deep Learning
par: Cerqueira, Vitor, et autres
Publié: (2024)

Learning Constrained Optimization with Deep Augmented Lagrangian Methods
par: Kotary, James, et autres
Publié: (2024)

Reevaluating Theoretical Analysis Methods for Optimization in Deep Learning
par: Tran, Hoang, et autres
Publié: (2024)

MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting
par: Cai, Wanlin, et autres
Publié: (2023)

DeepMIDE: A Multi-Output Spatio-Temporal Method for Ultra-Scale Offshore Wind Energy Forecasting
par: Ye, Feng, et autres
Publié: (2024)

DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models
par: Kudo, Atsushi
Publié: (2024)

Tin-Tin: Towards Tiny Learning on Tiny Devices with Integer-based Neural Network Training
par: Hu, Yi, et autres
Publié: (2025)

A study on performance limitations in Federated Learning
par: Mohan, Karthik
Publié: (2025)

A Granular Framework for Construction Material Price Forecasting: Econometric and Machine-Learning Approaches
par: Lyu, Boge, et autres
Publié: (2025)

HarmonE: A Self-Adaptive Approach to Architecting Sustainable MLOps
par: Bhatt, Hiya, et autres
Publié: (2025)

A Survey of Deep Learning and Foundation Models for Time Series Forecasting
par: Miller, John A., et autres
Publié: (2024)

One-Step Bellman Alignment Enables Provably Efficient Transfer in Online RL
par: Chen, Elynn, et autres
Publié: (2026)

Multi-Agent Stochastic Bandits Robust to Adversarial Corruptions
par: Ghaffari, Fatemeh, et autres
Publié: (2024)

AROpt: An Optimization Method for Autoregressive Time Series Forecasting
par: Li, Zheng, et autres
Publié: (2026)