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Demand Forecasting

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Showing 176–200 of 212 papers

TitleStatusHype
XAI-KG: knowledge graph to support XAI and decision-making in manufacturing—0
XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting with High Volatility—0
Deep Learning based Forecasting: a case study from the online fashion industry—0
A Bayesian Approach for Predicting Food and Beverage Sales in Staff Canteens and Restaurants—0
PA-CFL: Privacy-Adaptive Clustered Federated Learning for Transformer-Based Sales Forecasting on Heterogeneous Retail Data—0
A Clustering-aided Ensemble Method for Predicting Ridesourcing Demand in Chicago—0
A Comprehensive Forecasting Framework based on Multi-Stage Hierarchical Forecasting Reconciliation and Adjustment—0
A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting—0
Actionable Cognitive Twins for Decision Making in Manufacturing—0
A Daily Tourism Demand Prediction Framework Based on Multi-head Attention CNN: The Case of The Foreign Entrant in South Korea—0
Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things—0
Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data—0
AI-Based Demand Forecasting and Load Balancing for Optimising Energy use in Healthcare Systems: A real case study—0
Algorithmic Decision-Making Safeguarded by Human Knowledge—0
A machine learning approach to itinerary-level booking prediction in competitive airline markets—0
A New Decomposition Ensemble Approach for Tourism Demand Forecasting: Evidence from Major Source Countries—0
An Exponential Factorization Machine with Percentage Error Minimization to Retail Sales Forecasting—0
A novel forecasting framework combining virtual samples and enhanced Transformer models for tourism demand forecasting—0
A Novel Hybrid Approach to Contraceptive Demand Forecasting: Integrating Point Predictions with Probabilistic Distributions—0
Towards More Efficient Shared Autonomous Mobility: A Learning-Based Fleet Repositioning Approach—0
An XGBoost-Based Forecasting Framework for Product Cannibalization—0
Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting—0
Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting—0
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale—0
News and Load: A Quantitative Exploration of Natural Language Processing Applications for Forecasting Day-ahead Electricity System Demand—0
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