Cover Image

Hierarchical Industrial Demand Forecasting with Temporal and Uncertainty Explanations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kamarthi, Harshavardhan, Xu, Shangqing, Tong, Xinjie, Zhou, Xingyu, Peters, James, Czyzyk, Joseph, Prakash, B. Aditya
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Performative Time-Series Forecasting
by: Zhao, Zhiyuan, et al.
Published: (2023)

Efficient Contrastive Explanations on Demand
by: Izza, Yacine, et al.
Published: (2024)

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

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

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

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

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

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

Weakly Supervised Learning on Large Graphs
by: Prakash, Aditya
Published: (2025)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Hierarchical Forecasting at Scale
by: Sprangers, Olivier, et al.
Published: (2023)