arXiv Machine Learning

Interpretable Machine Learning for Traffic Congestion Prediction: Unveiling the Impact of Different COVID-19 Periods

arXiv:2608. 01180v1 Announce Type: new Abstract: Traffic congestion prediction is essential for congestion mitigation, but the COVID-19 pandemic and related control measures altered travel behavior and increased prediction complexity.

arXiv Machine Learning

Aug 7

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction

arXiv:2608. 05265v1 Announce Type: new Abstract: Prediction of post-wildfire debris flows is critical for mitigating hazards to communities, infrastructure, and resources during intense rainfall in recently burned areas.

By Quinn Ledingham, Zhengsen Xu, Yimin Zhu, Zack Dewis, Mabel Heffring, Saeid Taleghanidoozdoozan, Motasem Alkayid, Megan Greenwood, Lincoln Linlin Xu

benchmarks safety

arXiv Machine Learning

Jun 4

RIDE: An Open Dataset and Benchmark for Train Delay Prediction

arXiv:2606. 05070v1 Announce Type: new Abstract: Train delay prediction is an important problem for both passengers and railway operators, yet progress in the field remains difficult to assess due to the lack of standardized datasets, prediction targets, and evaluation protocols.

By Cl\'ement Elliker, Mathis Le Bail, Cl\'ement Mantoux, Jesse Read, Sonia Vanier

benchmarks data-engineering

arXiv Machine Learning

22h ago

Proactive Road Safety Intervention in Australia: Predicting Risky Driving Hotspots from Connected Vehicle Data

arXiv:2608. 16913v1 Announce Type: new Abstract: Road safety monitoring has historically been reactive, relying on crash-record analysis after fatalities and injuries have already occurred.

By Adriana-Simona Mih\u{a}i\c{t}\u{a}, Clarence Cheung, Artur Grigorev, Tuo Mao, David Lillo-Trynes

safety

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

Aug 4

DSETA: A Dual-Stage Continual Learning Framework for Travel Time Prediction in Dynamic Traffic Environments

arXiv:2608. 00402v1 Announce Type: new Abstract: Estimated Time of Arrival (ETA) prediction is a core component of intelligent transportation systems.

By Yanming Lyu, Yue Cheng, Lingkun Li, Ruipeng Gao, Xinyue Liu, Hui Gao, Qiang Ni

arXiv Machine Learning

Jul 14

Inter-Stop Energy Prediction and Causal Driver Quantification for Dual-Source Trolleybuses via a Time-Aware Tabular Deep Learning Architecture

arXiv:2607. 11349v1 Announce Type: cross Abstract: Dual-source trolleybuses alternate between overhead catenary supply and on-board battery operation, creating energy-use patterns driven by route attributes, high-frequency trajectories, and hourly weather.

By Wentao Zeng (School of Management, Foshan University, Foshan, China a School of Management, Foshan University, Foshan, China, School of Mechanical and Electrical Engineering and Automation, Foshan University, Foshan, China), Zijian Huang (School of Artificial Intelligence, South China Normal University, Guangzhou, China), Yiming Bie (School of Transportation, Jilin University, Changchun, China), Jiabin Wu (School of Management, Foshan University, Foshan, China a School of Management, Foshan University, Foshan, China), Jun Gong (Department of Civil Engineering, The University of Hong Kong, Hong Kong, China)

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

arXiv Machine Learning

Jun 2

Optimizing accuracy and diversity: a multi-task approach to forecast combinations

arXiv:2310. 20545v3 Announce Type: replace Abstract: We present a multi-task optimization approach based on a deep learning architecture for time series forecasting.

By Giovanni Felici, Antonio M. Sudoso

benchmarks

arXiv Machine Learning

Jul 31

Information Bottleneck Learning for Faithful Time Series Forecasting Explanations

arXiv:2607. 28124v1 Announce Type: new Abstract: As forecasts increasingly drive decisions in fields such as energy, transportation, and healthcare, understanding the historical data behind these predictions has become as crucial as the predictions themselves.

By Xu Zheng, Wei Cheng, Zhuomin Chen, Mo Sha, Jingchao Ni, Dongsheng Luo

efficiency

arXiv AI

Jun 30

When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregate

arXiv:2512. 03578v3 Announce Type: replace-cross Abstract: Time series extrinsic regression (TSER) refers to the task of predicting a continuous target variable from an input time series.

By Florent Forest, Amaury Wei, Olga Fink

benchmarks safety

arXiv Machine Learning

Jul 15

Predictive Modeling of High-Altitude Clear Air Turbulence in the United States: A Machine Learning Approach

arXiv:2607. 11899v1 Announce Type: cross Abstract: High-altitude Clear Air Turbulence (CAT) poses significant risks to aviation safety due to its unpredictability and challenges in detection.

By Kadir Gokdeniz, Irem Ulku

safety

arXiv AI

Jul 23

Time Series Network Utilization KPI Forecasting Using Advanced AI/ML Models

arXiv:2607. 19974v1 Announce Type: cross Abstract: The rapid proliferation of data-intensive applications, cloud infrastructure, and IoT ecosystems has made proactive resource provisioning critical for maintaining optimal network performance.

By Niraj Gadhe, Kirti Bhardwaj, Moulik Jain, Shubhi Sharma, Vinay Saini

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