Key papers

• Optimizing Regret (2026)
  Irene Aldridge
  4.39
• Measuring Time Series Forecast Stability for Demand Planning (2025)
  Steven Klee and Yuntian Xia
  3.92
• Advancing GDP Forecasting: The Potential of Machine Learning Techniques in Economic Predictions (2025)
  Bogdan Oancea
  3.59
• A Granular Framework for Construction Material Price Forecasting: Econometric and Machine-Learning Approaches (2025)
  Boge Lyu et al.
  3.21
• MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model (2024)
  Md Abrar Jahin et al.
  3.14
• 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
• Recovery-Informed Forecasting Strategy Enhancement (2026)
  Feng Li and Taozhu Ruan
  1.78
• Hierarchical Industrial Demand Forecasting with Temporal and Uncertainty Explanations (2026)
  Harshavardhan Kamarthi et al.
  1.78
• 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
• Automated univariate time series forecasting with regression trees (2026)
  Francisco Mart\'inez and Mar\'ia P. Fr\'ias
  1.72
• EventCast: Hybrid Demand Forecasting in E-Commerce with LLM-Based Event Knowledge (2026)
  Congcong Hu et al.
  1.72
• Regularized Ensemble Forecasting for Learning Weights from Historical and Current Forecasts (2026)
  Han Su et al.
  1.72
• Robust optimal reconciliation for hierarchical time series forecasting with M-estimation (2026)
  Zhichao Wang et al.
  1.72
• One Global Model, Many Behaviors: Stockout-Aware Feature Engineering and Dynamic Scaling for Multi-Horizon Retail Demand Forecasting with a Cost-Aware Ordering Policy (VN2 Winner Report) (2026)
  Bartosz Szab{\l}owski
  1.67
• From Many Models, One: Macroeconomic Forecasting with Reservoir Ensembles (2025)
  Giovanni Ballarin et al.
  1.61
• Automobile demand forecasting: Spatiotemporal and hierarchical modeling, life cycle dynamics, and user-generated online information (2025)
  Tom Nahrendorf et al.
  1.56
• Hierarchical Time Series Forecasting with Robust Reconciliation (2025)
  Shuhei Aikawa et al.
  1.50
• Forecast reconciliation with non-linear constraints (2025)
  Daniele Girolimetto et al.
  1.50
• Remaining Time Prediction in Outbound Warehouse Processes: A Case Study (Short Paper) (2025)
  Erik Penther et al.
  1.44
• OpenFPL: An open-source forecasting method rivaling state-of-the-art Fantasy Premier League services (2025)
  Daniel Groos
  1.39
• Robust Short-Term OEE Forecasting in Industry 4.0 via Topological Data Analysis (2025)
  Korkut Anapa et al.
  1.33
• Time Series Forecastability Measures (2025)
  Rui Wang et al.
  1.33
• On the retraining frequency of global models in retail demand forecasting (2025)
  Marco Zanotti
  1.22
• Empirical Comparison of Lightweight Forecasting Models for Seasonal and Non-Seasonal Time Series (2025)
  Thanh Son Nguyen et al.
  1.22
• ModelRadar: Aspect-based Forecast Evaluation (2025)
  Vitor Cerqueira et al.
  1.17
• Hurdle Network Model With Latent Dynamic Shrinkage For Enhanced Edge Prediction in Zero-Inflated Directed Network Time Series (2025)
  Sandipan Pramanik et al.
  1.17
• Forecasting Empty Container availability for Vehicle Booking System Application (2025)
  Arthur Cartel Foahom Gouabou (AMU et al.
  1.11
• Predicting Bad Goods Risk Scores with ARIMA Time Series: A Novel Risk Assessment Approach (2025)
  Bishwajit Prasad Gond
  1.06
• The ecological forecast limit revisited: Potential, actual and relative system predictability (2024)
  Marieke Wesselkamp et al.
  1.00
• Identification and estimation for matrix time series CP-factor models (2024)
  Jinyuan Chang et al.
  0.89
• Using dynamic loss weighting to boost improvements in forecast stability (2024)
  Daan Caljon et al.
  0.83
• Intervention-Aware Forecasting: Breaking Historical Limits from a System Perspective (2024)
  Zhijian Xu et al.
  0.61
• On Forecast Stability (2023)
  Rakshitha Godahewa et al.
  0.22
• Bayesian forecasting of electoral outcomes with new parties' competition (2016)
  Jos\'e Garc\'ia Montalvo and Omiros Papaspiliopoulos and Timoth\'ee Stumpf-F\'etizon
  0.00
• The impact of stochastic lead times on the bullwhip effect under correlated demand and moving average forecasts (2017)
  Zbigniew Michna et al.
  —
• Note on Evolution and Forecasting of Requirements: Communications Example (2017)
  Mark Sh. Levin
  —
• Sales Forecast in E-commerce using Convolutional Neural Network (2017)
  Kui Zhao et al.
  —
• Forecasting Player Behavioral Data and Simulating in-Game Events (2017)
  Anna Guitart et al.
  —
• ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting (2018)
  Srayanta Mukherjee et al.
  —
• Aggregating Strategies for Long-term Forecasting (2018)
  Alexander Korotin and Vladimir V'yugin and Evgeny Burnaev
  —
• Predicting football tables by a maximally parsimonious model (2018)
  Kjetil Haugen and Brynjulf Owren
  —
• Leveraging Elastic Demand for Forecasting (2018)
  Houtao Deng et al.
  —
• Day-ahead time series forecasting: application to capacity planning (2018)
  Colin Leverger (LACODAM) et al.
  —
• Consistency of Forecasts for the U.S. House of Representatives (2018)
  Henry Bendekgey
  —
• Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology (2019)
  Kasun Bandara et al.
  —
• Optimal Combination Forecasts on Retail Multi-Dimensional Sales Data (2019)
  Luis Roque et al.
  —
• Forecasting Weakly Correlated Time Series in Tasks of Electronic Commerce (2019)
  Lyudmyla Kirichenko and Tamara Radivilova and Illya Zinkevich
  —
• Demand forecasting techniques for build-to-order lean manufacturing supply chains (2019)
  Rodrigo Rivera-Castro and Ivan Nazarov and Yuke Xiang and Alexander Pletneev and Ivan Maksimov and Evgeny Burnaev
  —
• A multi-series framework for demand forecasts in E-commerce (2019)
  R\'emy Garnier and Arnaud Belletoile
  —
• Adaptive Variable Selection for Sequential Prediction in Multivariate Dynamic Models (2019)
  Isaac Lavine et al.
  —
• A Self-supervised Approach to Hierarchical Forecasting with Applications to Groupwise Synthetic Controls (2019)
  Konstantin Mishchenko et al.
  —
• Demand Forecasting in the Presence of Systematic Events: Cases in Capturing Sales Promotions (2019)
  Mahdi Abolghasemi et al.
  —
• Time Series Modeling for Dream Team in Fantasy Premier League (2019)
  Akhil Gupta
  —
• Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion (2019)
  Mahdi Abolghasemi et al.
  —
• Application of Machine Learning in Forecasting International Trade Trends (2019)
  Feras Batarseh et al.
  —
• Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective (2020)
  Tine Van Calster et al.
  —
• Seasonal and Trend Forecasting of Tourist Arrivals: An Adaptive Multiscale Ensemble Learning Approach (2020)
  Shaolong Suna et al.
  —
• How Much Can A Retailer Sell? Sales Forecasting on Tmall (2020)
  Chaochao Chen et al.
  —