·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
China delivers a one-two punch to America’s AI dominance5h◆AI is more likely than humans to form biases when hiring6h◆Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal11h◆SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents11h◆PolyInterview: An LLM-based Platform for Immersive Mock Interview Practice with Comprehensive Multimodal Assessment11h◆Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length11h◆Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications11h◆DyneTrion: A Spatio-temporally Coherent Generative Emulator for Protein Dynamics Across Timescales11h◆Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors11h◆CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data11h◆cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data11h◆Do Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?11h◆Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes11h◆From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems11h◆Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI11h◆The AI Fiction Paradox11h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents11h◆EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections11h◆Before the Action: Benchmarking LLMs on Prospective Hypothesis Discovery11h◆How Much Human Label Variation Does Formal Semantic Structure Explain?: Group-Level Effects and Item-Level Ceilings in NLI11h◆China delivers a one-two punch to America’s AI dominance5h◆AI is more likely than humans to form biases when hiring6h◆Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal11h◆SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents11h◆PolyInterview: An LLM-based Platform for Immersive Mock Interview Practice with Comprehensive Multimodal Assessment11h◆Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length11h◆Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications11h◆DyneTrion: A Spatio-temporally Coherent Generative Emulator for Protein Dynamics Across Timescales11h◆Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors11h◆CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data11h◆cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data11h◆Do Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?11h◆Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes11h◆From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems11h◆Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI11h◆The AI Fiction Paradox11h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents11h◆EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections11h◆Before the Action: Benchmarking LLMs on Prospective Hypothesis Discovery11h◆How Much Human Label Variation Does Formal Semantic Structure Explain?: Group-Level Effects and Item-Level Ceilings in NLI11h◆
News/Improving Coherence in Hierarchical Time Series Forecasting using Structured Temporal Fusion
arxiv
PublishedJune 30, 2026 at 4:00 AM

Improving Coherence in Hierarchical Time Series Forecasting using Structured Temporal Fusion

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.28553v1 Announce Type: new Abstract: In many real-world applications, such as retail sales, energy usage, and supply chain planning, forecasting is performed across hierarchical structures. These structures often represent aggregations (e.g., products to categories to regions), where fore

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivBeyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal11harxivSkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents11harxivPolyInterview: An LLM-based Platform for Immersive Mock Interview Practice with Comprehensive Multimodal Assessment11harxivLatency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length11h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews