arXiv Machine Learning

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.

arXiv Machine Learning

Jun 19

Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting

arXiv:2606. 19560v1 Announce Type: new Abstract: Seasonal influenza infects millions of people and causes substantial morbidity and mortality in the United States each year, making accurate short-term forecasting a core public-health need.

By Alireza Jafari, Judy Fox, Geoffrey C. Fox, Madhav Marathe, Aniruddha Adiga

llms

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

Jul 15

Auditable Context-Aware HFMD Forecasting with Structured LLM Agents

arXiv:2511. 23276v2 Announce Type: replace Abstract: Effective HFMD surveillance requires forecasts capturing both time-series patterns and contextual drivers such as school calendars, weather, and policy or surveillance reports.

By Joongwon Chae, Runming Wang, Chen Xiong, Gong Yunhan, Lian Zhang, Ji Jiansong, Dongmei Yu, Peiwu Qin

llms agents

arXiv Machine Learning

Jun 10

One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data

arXiv:2606. 10678v1 Announce Type: new Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies.

By Amrijit Biswas, Mustafa Kamal, Robin Krambroeckers, M. M. Lutfe Elahi, Sifat Momen, Nabeel Mohammed, Shafin Rahman

llms benchmarks safety

arXiv AI

Jun 10

CITRAS: Covariate-Informed Transformer for Time Series Forecasting

arXiv:2503. 24007v4 Announce Type: replace-cross Abstract: In time series forecasting, covariates represent external factors that influence target variables.

By Yosuke Yamaguchi, Issei Suemitsu, Wenpeng Wei

llms

arXiv Machine Learning

Jun 8

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

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

By Dang Viet Anh Nguyen, Alma Fazlagic, Kristine Pryds Loft, Filipe Rodrigues

arXiv Machine Learning

Jul 31

Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework

arXiv:2607. 28035v1 Announce Type: new Abstract: Irregular multivariate time series are widely encountered in applications such as healthcare monitoring, human activity recognition, and environmental sensing.

By Tianen Shen, Zhengyu Li, Yutong Li, Xiangfei Qiu, Xingjian Wu, Bin Yang, Jilin Hu

llms diffusion nlp benchmarks

arXiv Machine Learning

Jul 2

When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting

arXiv:2607. 01082v1 Announce Type: new Abstract: Spatio-temporal point-process models must often generalise across space when local event histories are sparse.

By Yahya Aalaila, Mouad Elhamdi, Gerrit Gro{\ss}mann, Daniel Jenson, Elizaveta Semenova, Sebastian Vollmer

rag

arXiv Machine Learning

Jul 20

DELUGE: Towards Continental-Scale Daily Pluvial Flood Damage Prediction via Interpretable Conditioning on Foundation Model Embeddings

arXiv:2607. 16050v1 Announce Type: new Abstract: Pluvial (rainfall-driven) flooding accounts for 45% of National Flood Insurance Program (NFIP) claims in the United States and is harder to predict than its riverine and coastal counterparts, with existing approaches limited to coarse resolution, regional domains, or computationally intensive process-based models unsuitable for daily continental-scale use.

By Yuya Kawakami, Daniel Cayan, Dongyu Liu, Kwan-Liu Ma, Tom Corringham

rag multimodal safety

arXiv Machine Learning

Jul 20

LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Models

arXiv:2607. 15447v1 Announce Type: new Abstract: Recent research in clinical machine learning, focusing on outcome predictions in intensive care unit (ICU), has shifted from bespoke supervised models to foundation models, utilising modern representation learning methods.

By Jingteng Li, Alexander Capstick, Louise Rigny, Iona Biggart, Neil J Sebire, Payam Barnaghi

llms rag benchmarks

arXiv Machine Learning

Aug 12

GARLIC: Graph Attention-based Relational Learning of Multivariate Time Series in Intensive Care

arXiv:2608. 10969v1 Announce Type: new Abstract: Healthcare data, such as Intensive Care Unit (ICU) records, comprise heterogeneous multivariate time series sampled at irregular intervals with pervasive missingness.

By Ruirui Wang, Yanke Li, Manuel G\"unther, Diego Paez-Granados

benchmarks

arXiv Machine Learning

Jun 11

CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting

arXiv:2511. 09789v2 Announce Type: replace Abstract: Recent advances in deep forecasting models have achieved remarkable performance, yet most approaches still struggle to provide both accurate predictions and interpretable insights into temporal dynamics.

By Fulong Yao, Wanqing Zhao, Chao Zheng, Xiaofei Han

llms benchmarks

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