Mobile demand forecasting via deep graph-sequence spatiotemporal modeling in cellular networks

Keyphrases

• Cellular Networks 100%
• Spatio-temporal Modeling 100%
• Demand Forecasting 100%
• Graph Sequence 100%
• Mobile Demand 100%
• Temporal Aspects 42%
• Spatial Aspect 42%
• Spatial Distance 42%
• Spatial Granularity 28%
• Graph Convolutional Network 28%
• Spatial Relevancy 28%
• Spatial Distribution 14%
• Network Infrastructure 14%
• Spatiotemporal Analysis 14%
• Distance-based 14%
• Physical Network 14%
• Network Management 14%
• Deep Learning 14%
• Virtual Network 14%
• Two-cell 14%
• Grid-based Model 14%
• Time Series Model 14%
• Semivariogram 14%
• Graph-based 14%
• Forecast Performance 14%
• Capital Expenditure (CAPEX) 14%
• Efficient Modeling 14%
• Dependency Graph 14%
• Graphical Modeling 14%
• Convolutional Neural Network 14%
• Operational Expenditure 14%
• Long Short-term Memory 14%
• Convolutional Long Short-term Memory (ConvLSTM) 14%
• Sequence Models 14%
• Auto Regression Integrated Moving Average 14%

Computer Science

• Cellular Network 100%
• Granularity 66%
• Long Short-Term Memory Network 66%
• Network Infrastructure 33%
• Network Management 33%
• Convolutional Neural Network 33%
• Effective Modeling 33%
• Sequence Model 33%
• Graph Convolutional Network 33%
• Operational Expenditure 33%
• Spatial Distribution 33%
• Moving Average 33%
• Dependency Graph 33%
• Deep Learning 33%

Neuroscience

• Short-Term Memory 100%
• Neural Network 50%
• Time Series Model 50%