Key papers

• Research on Metro Transportation Flow Prediction Based on the STL-GRU Combined Model (2025)
  Zijie Zhou et al.
  6.23
• Avocado Price Prediction Using a Hybrid Deep Learning Model: TCN-MLP-Attention Architecture (2025)
  Linwei Zhang et al.
  6.01
• Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition (2026)
  Jing Jie Tan et al.
  5.89
• Optimizing Supply Chain Networks with the Power of Graph Neural Networks (2025)
  Chi-Sheng Chen et al.
  5.79
• Real-time Air Pollution prediction model based on Spatiotemporal Big data (2018)
  Van-Duc Le et al.
  5.52
• Trajectory Data Mining and Trip Travel Time Prediction on Specific Roads (2024)
  Muhammad Awais Amin et al.
  4.85
• Forecasting Labor Markets with LSTNet: A Multi-Scale Deep Learning Approach (2025)
  Adam Nelson-Archer et al.
  4.53
• Governing Well in the Algorithmic Age: The Foundations of Digital Statecraft (2026)
  Zeynep Engin et al.
  4.39
• Deep Learning-Based Forecasting of Hotel KPIs: A Cross-City Analysis of Global Urban Markets (2025)
  C. J. Atapattu et al.
  3.86
• Traffic Congestion Prediction Using Machine Learning Techniques (2022)
  Rafed Muhammad Yasir et al.
  3.71
• Combating the Bullwhip Effect in Rival Online Food Delivery Platforms Using Deep Learning (2025)
  Tisha Ghosh
  3.64
• MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model (2024)
  Md Abrar Jahin et al.
  3.14
• Deep Learning for Crime Forecasting: The Role of Mobility at Fine-grained Spatiotemporal Scales (2025)
  Ariadna Albors Zumel et al.
  3.04
• When Simpler Wins: Facebooks Prophet vs LSTM for Air Pollution Forecasting in Data-Constrained Northern Nigeria (2025)
  Habeeb Balogun and Yahaya Zakari
  2.99
• Spatio-Temporal Demand Prediction for Food Delivery Using Attention-Driven Graph Neural Networks (2025)
  Rabia Latief Bhat and Iqra Altaf Gillani
  2.93
• Forecasting Labor Demand: Predicting JOLT Job Openings using Deep Learning Model (2025)
  Kyungsu Kim
  2.71
• Unlocking air traffic flow prediction through microscopic aircraft-state modeling (2026)
  Bin Wang et al.
  1.89
• Dual-Temporal LSTM with Hybrid Attention for Airline Passenger Load Factor Forecasting: Integrating Intra-Flight and Inter-Flight Booking Dynamics (2026)
  ASM Nazrul Islam et al.
  1.89
• Bridging Deep Learning and Integer Linear Programming: A Predictive-to-Prescriptive Framework for Supply Chain Analytics (2026)
  Khai Banh Nghiep and Duc Nguyen Minh and Lan Hoang Thi
  1.83
• Hybrid Deep Learning Approach for Coupled Demand Forecasting and Supply Chain Optimization (2026)
  Nusrat Yasmin Nadia et al.
  1.83
• Competition-Aware CPC Forecasting with Near-Market Coverage (2026)
  Sebastian Frey et al.
  1.78
• The impact of machine learning forecasting on strategic decision-making for Bike Sharing Systems (2026)
  Enrico Angelelli et al.
  1.78
• EventCast: Hybrid Demand Forecasting in E-Commerce with LLM-Based Event Knowledge (2026)
  Congcong Hu et al.
  1.72
• TUBO: A Tailored ML Framework for Reliable Network Traffic Forecasting (2026)
  Zhihang Yuan et al.
  1.72
• MoDE-Boost: Boosting Shared Mobility Demand with Edge-Ready Prediction Models (2026)
  Antonios Tziorvas et al.
  1.72
• Bikelution: Federated Gradient-Boosting for Scalable Shared Micro-Mobility Demand Forecasting (2026)
  Antonios Tziorvas et al.
  1.72
• Forecasting MBTA Transit Dynamics: A Performance Benchmarking of Statistical and Machine Learning Models (2025)
  Sai Siddharth Nalamalpu and Kaining Yuan and Aiden Zhou and Eugene Pinsky
  1.61
• Semiconductor Industry Trend Prediction with Event Intervention Based on LSTM Model in Sentiment-Enhanced Time Series Data (2025)
  Wei-hsiang Yen and Lyn Chao-ling Chen
  1.56
• Automobile demand forecasting: Spatiotemporal and hierarchical modeling, life cycle dynamics, and user-generated online information (2025)
  Tom Nahrendorf et al.
  1.56
• TTF: A Trapezoidal Temporal Fusion Framework for LTV Forecasting in Douyin (2025)
  Yibing Wan et al.
  1.56
• Leveraging Spatiotemporal Graph Neural Networks for Multi-Store Sales Forecasting (2025)
  Manish Singh et al.
  1.56
• ACA-Net: Future Graph Learning for Logistical Demand-Supply Forecasting (2025)
  Jiacheng Shi and Haibin Wei and Jiang Wang and Xiaowei Xu and Longzhi Du and Taixu Jiang
  1.44
• Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models (2025)
  Jonathan Teagan
  1.44
• Remaining Time Prediction in Outbound Warehouse Processes: A Case Study (Short Paper) (2025)
  Erik Penther et al.
  1.44
• Neuroplasticity-inspired dynamic ANNs for multi-task demand forecasting (2025)
  Mateusz \.Zarski and S{\l}awomir Nowaczyk
  1.44
• A Data-Driven Approach to Estimate LEO Orbit Capacity Models (2025)
  Braden Stock et al.
  1.33
• Forecasting Empty Container availability for Vehicle Booking System Application (2025)
  Arthur Cartel Foahom Gouabou (AMU et al.
  1.11
• CoNOAir: A Neural Operator for Forecasting Carbon Monoxide Evolution in Cities (2025)
  Sanchit Bedi (1) et al.
  1.00
• $\spadesuit$ SPADE $\spadesuit$ Split Peak Attention DEcomposition (2024)
  Malcolm Wolff et al.
  0.94
• Predicting Process Behaviour using Deep Learning (2016)
  Joerg Evermann et al.
  —
• An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service (2017)
  Isma\"il Saadi et al.
  —
• Machine learning modeling for time series problem: Predicting flight ticket prices (2017)
  Jun Lu
  —
• Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach (2017)
  Jintao Ke et al.
  —
• Sales Forecast in E-commerce using Convolutional Neural Network (2017)
  Kui Zhao et al.
  —
• House Price Prediction Using LSTM (2017)
  Xiaochen Chen et al.
  —
• Forecasting Player Behavioral Data and Simulating in-Game Events (2017)
  Anna Guitart et al.
  —
• Deep Learning in Multiple Multistep Time Series Prediction (2017)
  Chuanyun Zang
  —
• Deep Learning applied to Road Traffic Speed forecasting (2017)
  Thomas Epelbaum (IPHT) et al.
  —
• LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances (2017)
  Nicol\`o Navarin et al.
  —
• Spatio-temporal Modeling of Yellow Taxi Demands in New York City Using Generalized STAR Models (2017)
  Abolfazl Safikhani et al.
  —
• Predicting Short-Term Uber Demand Using Spatio-Temporal Modeling: A New York City Case Study (2017)
  Sabiheh Sadat Faghih et al.
  —
• Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction (2018)
  Huaxiu Yao et al.
  —
• ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting (2018)
  Srayanta Mukherjee et al.
  —
• Forecasting Crime with Deep Learning (2018)
  Alexander Stec and Diego Klabjan
  —
• Predicting Learning Status in MOOCs using LSTM (2018)
  Zhemin Liu and Feng Xiong and Kaifa Zou and Hongzhi Wang
  —
• Spatio-temporal prediction of crimes using network analytic approach (2018)
  Saroj Kumar Dash and Ilya Safro and Ravisutha Sakrepatna Srinivasamurthy
  —
• Forecasting Granular Audience Size for Online Advertising (2019)
  Ritwik Sinha et al.
  —
• Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology (2019)
  Kasun Bandara et al.
  —
• Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks (2019)
  Alireza Nejadettehad et al.
  —
• Data-Driven Vehicle Trajectory Forecasting (2019)
  Shayan Jawed et al.
  —