Papers Demand Forecasting
“Demand Forecasting” 태그가 달린 논문 212편 · 필터 해제
AI-Based Demand Forecasting and Load Balancing for Optimising Energy use in Healthcare Systems: A real case study
This paper tackles the urgent need for efficient energy management in healthcare facilities, where fluctuating demands challenge operational efficiency and sustainability. Traditional methods often prove inadequate, caus…
Demand Forecastingenergy managementManagementTime Series ForecastingShort-Term Power Demand Forecasting for Diverse Consumer Types to Enhance Grid Planning and Synchronisation
Ensuring grid stability in the transition to renewable energy sources requires accurate power demand forecasting. This study addresses the need for precise forecasting by differentiating among industrial, commercial, and…
Demand ForecastingEarth Observationfeature selectionLoad ForecastingADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting
Passenger demand forecasting helps optimize vehicle scheduling, thereby improving urban efficiency. Recently, attention-based methods have been used to adequately capture the dynamic nature of spatio-temporal data. Howev…
Demand ForecastingDenoisingSchedulingFreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail
Accurate demand estimation is critical for the retail business in guiding the inventory and pricing policies of perishable products. However, it faces fundamental challenges from censored sales data during stockouts, whe…
Demand ForecastingImputationScalability Optimization in Cloud-Based AI Inference Services: Strategies for Real-Time Load Balancing and Automated Scaling
The rapid expansion of AI inference services in the cloud necessitates a robust scalability solution to manage dynamic workloads and maintain high performance. This study proposes a comprehensive scalability optimization…
Decision MakingDemand ForecastingWhy do zeroes happen? A model-based approach for demand classification
Effective demand forecasting is critical for inventory management, production planning, and decision making across industries. Selecting the appropriate model and suitable features to efficiently capture patterns in the …
Decision MakingDemand ForecastingAutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting
Electricity demand forecasting is key to ensuring that supply meets demand lest the grid would blackout. Reliable short-term forecasts may be obtained by combining a Generalized Additive Models (GAM) with a State-Space m…
Additive modelsAutoMLDemand ForecastingModel Selection+1SPDNet: Seasonal-Periodic Decomposition Network for Advanced Residential Demand Forecasting
Residential electricity demand forecasting is critical for efficient energy management and grid stability. Accurate predictions enable utility companies to optimize planning and operations. However, real-world residentia…
Computational EfficiencyDemand Forecastingenergy managementCombating the Bullwhip Effect in Rival Online Food Delivery Platforms Using Deep Learning
The wastage of perishable items has led to significant health and economic crises, increasing business uncertainty and fluctuating customer demand. This issue is worsened by online food delivery services, where frequent …
Demand ForecastingLeForecast: Enterprise Hybrid Forecast by Time Series Intelligence
Demand is spiking in industrial fields for multidisciplinary forecasting, where a broad spectrum of sectors needs planning and forecasts to streamline intelligent business management, such as demand forecasting, product …
Demand ForecastingTime SeriesTime Series ForecastingA novel forecasting framework combining virtual samples and enhanced Transformer models for tourism demand forecasting
Accurate tourism demand forecasting is hindered by limited historical data and complex spatiotemporal dependencies among tourist origins. A novel forecasting framework integrating virtual sample generation and a novel Tr…
Demand ForecastingManagementPA-CFL: Privacy-Adaptive Clustered Federated Learning for Transformer-Based Sales Forecasting on Heterogeneous Retail Data
Federated learning (FL) enables retailers to share model parameters for demand forecasting while maintaining privacy. However, heterogeneous data across diverse regions, driven by factors such as varying consumer behavio…
Demand ForecastingFeature ImportanceFederated LearningPrivacy Preserving+1Generative AI in Transportation Planning: A Survey
The integration of generative artificial intelligence (GenAI) into transportation planning has the potential to revolutionize tasks such as demand forecasting, infrastructure design, policy evaluation, and traffic simula…
Demand ForecastingDescriptiveRetrieval-augmented GenerationSurvey+1Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting
This paper presents an advanced Federated Learning (FL) framework for forecasting complex spatiotemporal data, improving upon recent state-of-the-art models. In the proposed approach, the original Gated Recurrent Unit (G…
Demand ForecastingFederated LearningPrivacy PreservingPredicting Space Tourism Demand Using Explainable AI
Comprehensive forecasts of space tourism demand are crucial for businesses to optimize strategies and customer experiences in this burgeoning industry. Traditional methods struggle to capture the complex factors influenc…
Demand ForecastingMarketingHow Do Consumers Really Choose: Exposing Hidden Preferences with the Mixture of Experts Model
Understanding consumer choice is fundamental to marketing and management research, as firms increasingly seek to personalize offerings and optimize customer engagement. Traditional choice modeling frameworks, such as mul…
Decision MakingDemand ForecastingMarketingMixture-of-ExpertsH-FLTN: A Privacy-Preserving Hierarchical Framework for Electric Vehicle Spatio-Temporal Charge Prediction
The widespread adoption of Electric Vehicles (EVs) poses critical challenges for energy providers, particularly in predicting charging time (temporal prediction), ensuring user privacy, and managing resources efficiently…
Demand ForecastingFederated LearningManagementPrivacy PreservingDemand Forecasting for Electric Vehicle Charging Stations using Multivariate Time-Series Analysis
As the number of electric vehicles (EVs) continues to grow, the demand for charging stations is also increasing, leading to challenges such as long wait times and insufficient infrastructure. High-precision forecasting o…
Decision MakingDemand ForecastingTime SeriesTime Series AnalysisForecasting time series with constraints
Time series forecasting presents unique challenges that limit the effectiveness of traditional machine learning algorithms. To address these limitations, various approaches have incorporated linear constraints into learn…
Additive modelsBenchmarkingDemand ForecastingTime Series+1A Novel Hybrid Approach to Contraceptive Demand Forecasting: Integrating Point Predictions with Probabilistic Distributions
Accurate demand forecasting is vital for ensuring reliable access to contraceptive products, supporting key processes like procurement, inventory, and distribution. However, forecasting contraceptive demand in developing…
Demand ForecastingHumanitarianTime Series