A Hybrid Prediction Method for Realistic Network Traffic With Temporal Convolutional Network and LSTM

Keyphrases

• Business Needs 12%
• Cloud Data Center 25%
• Computing Cloud 12%
• Current Prediction 12%
• Early Convergence 12%
• Edge Computing 12%
• Gradient Disappearance 12%
• Health Status 12%
• Help Systems 12%
• High-precision Prediction 12%
• Hybrid Forecasting Model 100%
• Industrial Area 12%
• Large-scale Networks 12%
• Local Features 12%
• Long Short-term Memory 100%
• Long Short-term Memory Neural Network 12%
• Long-range Dependence 37%
• Low-frequency Information 12%
• Network Applications 25%
• Network Scale 12%
• Network Traffic 100%
• Network Traffic Prediction 12%
• Neural Network 12%
• Nonlinear Characteristics 12%
• Nonlinear Features 12%
• Novel Hybrids 12%
• Prediction Accuracy 25%
• Prediction Method 12%
• Real-life Dataset 12%
• Result-oriented 12%
• Savitzky-Golay 25%
• Savitzky-Golay Filter 37%
• Short-term Features 12%
• Smart City 12%
• Strong Nonlinear 12%
• System Operator 12%
• Temporal Convolutional Network 100%
• Time Prediction 12%
• Time Series Data 25%
• Time Series Forecasting 12%
• Unstable Characteristic 12%

Computer Science

• Biggest Challenge 10%
• Cloud Computing 10%
• Data Center 10%
• Edge Computing 10%
• Experimental Result 20%
• Frequency Information 10%
• local feature 10%
• Long Short-Term Memory Network 100%
• Long Short-Term Memory Neural Network 10%
• Network Traffic 100%
• Neural Network 10%
• Prediction Accuracy 30%
• Prediction Time 10%
• Series Prediction 10%
• Smart City 10%
• Temporal Convolutional Network 100%
• Time Series Data 20%
• Time Series Smoothing 10%
• Traffic Prediction 10%