A novel hybrid framework based on temporal convolution network and transformer for network traffic prediction.

ABSTRACT:

Background

Accurately predicting mobile network traffic can help mobile network operators allocate resources more rationally and can facilitate stable and fast network services to users. However, due to burstiness and uncertainty, it is difficult to accurately predict network traffic.

Methodology

Considering the spatio-temporal correlation of network traffic, we proposed a deep-learning model, Convolutional Block Attention Module (CBAM) Spatio-Temporal Convolution Network-Transformer, for time-series prediction based on a CBAM attention mechanism, a Temporal Convolutional Network (TCN), and Transformer with a sparse self-attention mechanism. The model can be used to extract the spatio-temporal features of network traffic for prediction. First, we used the improved TCN for sp

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC10490908 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature