Imagen de Portada

Hierarchical Industrial Demand Forecasting with Temporal and Uncertainty Explanations

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Kamarthi, Harshavardhan, Xu, Shangqing, Tong, Xinjie, Zhou, Xingyu, Peters, James, Czyzyk, Joseph, Prakash, B. Aditya
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity
por: Kamarthi, Harshavardhan, et al.
Publicado: (2024)

In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks
por: Xu, Shangqing, et al.
Publicado: (2026)

Large Pre-trained time series models for cross-domain Time series analysis tasks
por: Kamarthi, Harshavardhan, et al.
Publicado: (2023)

Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting
por: Kamarthi, Harshavardhan, et al.
Publicado: (2024)

Seeking Universal Shot Language Understanding Solutions
por: Liu, Haoxin, et al.
Publicado: (2026)

Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
por: Liu, Haoxin, et al.
Publicado: (2024)

How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlook
por: Liu, Haoxin, et al.
Publicado: (2025)

Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis
por: Liu, Haoxin, et al.
Publicado: (2024)

TimeRecipe: A Time-Series Forecasting Recipe via Benchmarking Module Level Effectiveness
por: Zhao, Zhiyuan, et al.
Publicado: (2025)

Feature Rivalry in Sparse Autoencoder Representations: A Mechanistic Study of Uncertainty-Driven Feature Competition in LLMs
por: Harshavardhan
Publicado: (2026)

TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecasting
por: Zhao, Zhiyuan, et al.
Publicado: (2025)

LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting
por: Liu, Haoxin, et al.
Publicado: (2024)

Tackling Time-Series Forecasting Generalization via Mitigating Concept Drift
por: Zhao, Zhiyuan, et al.
Publicado: (2025)

Coherent Hierarchical Probabilistic Forecasting of Electric Vehicle Charging Demand
por: Zheng, Kedi, et al.
Publicado: (2024)

Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting
por: Liu, Haoxin, et al.
Publicado: (2026)

Spatial Uncertainty Quantification in Wildfire Forecasting for Climate-Resilient Emergency Planning
por: Chakravarty, Aditya
Publicado: (2025)

Performative Time-Series Forecasting
por: Zhao, Zhiyuan, et al.
Publicado: (2023)

Efficient Contrastive Explanations on Demand
por: Izza, Yacine, et al.
Publicado: (2024)

Temporal Data Requirement for Predicting Unplanned Hospital Readmissions
por: Mohammadi, Ramin, et al.
Publicado: (2026)

Hierarchical Neural Additive Models for Interpretable Demand Forecasts
por: Feddersen, Leif, et al.
Publicado: (2024)

Modular Deep-Learning-Based Early Warning System for Deadly Heatwave Prediction
por: Xu, Shangqing, et al.
Publicado: (2025)

Taxonomy-Conditioned Hierarchical Bayesian TSB Models for Heterogeneous Intermittent Demand Forecasting
por: Bai, Zong-Han, et al.
Publicado: (2025)

VMDNet: Temporal Leakage-Free Variational Mode Decomposition for Electricity Demand Forecasting
por: Feng, Weibin, et al.
Publicado: (2025)

Evaluating System 1 vs. 2 Reasoning Approaches for Zero-Shot Time Series Forecasting: A Benchmark and Insights
por: Liu, Haoxin, et al.
Publicado: (2025)

Weakly Supervised Learning on Large Graphs
por: Prakash, Aditya
Publicado: (2025)

Prediction of Clinical Complication Onset using Neural Point Processes
por: Weerasekara, Sachini, et al.
Publicado: (2025)

A Spatio-Temporal Feature Fusion EEG Virtual Channel Signal Generation Network and Its Application in Anxiety Assessment
por: Yuan, Shangqing, et al.
Publicado: (2025)

Sliding-Window Signatures for Time Series: Application to Electricity Demand Forecasting
por: Drobac, Nina, et al.
Publicado: (2025)

How Understanding Forecast Uncertainty Resolves the Explainability Problem in Machine Learning Models
por: Breeden, Joseph L.
Publicado: (2026)

LightSAE: Parameter-Efficient and Heterogeneity-Aware Embedding for IoT Multivariate Time Series Forecasting
por: Ren, Yi, et al.
Publicado: (2025)

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail
por: Wang, Yangyang, et al.
Publicado: (2025)

Bimanual 3D Hand Motion and Articulation Forecasting in Everyday Images
por: Prakash, Aditya, et al.
Publicado: (2025)

Beyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecasting
por: Wang, Yijun, et al.
Publicado: (2026)

The Uncertainty of Machine Learning Predictions in Asset Pricing
por: Liao, Yuan, et al.
Publicado: (2025)

Spatio-Temporal Hierarchical Causal Models
por: Li, Xintong, et al.
Publicado: (2025)

Probabilistic Demand Forecasting with Graph Neural Networks
por: Kozodoi, Nikita, et al.
Publicado: (2024)

Sanity Checks for Explanation Uncertainty
por: Valdenegro-Toro, Matias, et al.
Publicado: (2024)

TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting
por: Feng, Shibo, et al.
Publicado: (2025)

Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space
por: Cai, Li, et al.
Publicado: (2024)

Hierarchical Forecasting at Scale
por: Sprangers, Olivier, et al.
Publicado: (2023)