arXiv Machine Learning June 8, 2026 By Dang Viet Anh Nguyen, Alma Fazlagic, Kristine Pryds Loft, Filipe Rodrigues

Hierarchical Forecast Reconciliation for Urban Rail Transit Demand Prediction under Operational Disruptions

Read the original on arXiv Machine Learning →

arXiv:2606. 07044v1 Announce Type: new Abstract: Accurate and coherent passenger demand forecasting is essential for Urban Rail Transit (URT) operations.

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arXiv Machine Learning

Jul 30

Hierarchical Spatio-Temporal Transformer for Coherent Emergency Department Forecasting

arXiv:2607. 27106v1 Announce Type: new Abstract: Emergency Departments (EDs) are critical access points in healthcare systems, yet they face persistent pressure from unpredictable patient demand, seasonal surges, and non-urgent visits.

By Filipa Lino, B\'arbara Tavares, Carlos Santiago, Cl\'audia Soares, Manuel Marques

llms

arXiv Machine Learning

Jun 4

REGAIN: REconciliation GAIN-driven Auxiliary Direction Learning

arXiv:2606. 04380v1 Announce Type: cross Abstract: Forecast reconciliation usually starts from a fixed measurement system and asks how forecasts should be projected onto a coherent space.

By Weijia Li, Shun Hu, Yanfei Kang

safety

arXiv AI

Sep 4

RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting

RATL is a plug‑in method for multivariate time‑series forecasting that uses a frozen base forecaster to build a memory of its historical forecast residuals. During inference, RATL retrieves residual trajectories from similar past contexts and employs a set‑aware router to combine them, providing learned feedback correction. Experiments demonstrate that this residual‑retrieval approach improves the performance of the base forecaster across various benchmarks and backbones.

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REGAIN: REconciliation GAIN-driven Auxiliary Direction Learning

Forecast reconciliation usually starts from a fixed measurement system and asks how forecasts should be projected onto a coherent space. We ask a different question: which additional linear measurements should be forecast and included in the reconciliation system?

safety

arXiv Machine Learning

Jun 30

Improving Coherence in Hierarchical Time Series Forecasting using Structured Temporal Fusion

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.

By Ruchi Pakhle

llms rag benchmarks

arXiv Machine Learning

Jun 2

Spatiotemporal Multi-Task Graph Transformer for Trip-Level Transit Prediction

arXiv:2606. 00572v1 Announce Type: new Abstract: Passenger count data from public transit systems reveals urban mobility patterns and is essential for planning, operation, and optimisation.

By Oluwaleke Yusuf, Adil Rasheed, Frank Lindseth

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