Network traffic prediction based on spatial-temporal features cross fusion
Accurate network traffic prediction plays an important role in the rational allocation of network resources and the im-provement of communication quality.However,network traffic has complex spatial-temporal dependencies,presenting a high degree of nonlinearity and complexity,which brings difficulties to traffic prediction.After studying existing literature on network traffic pre-diction,analyzing the temporal and spatial properties of network traffic,a network traffic prediction model STCFusion based on the cross fusion of spatial-temporal features is proposed.And sufficient experiments were conducted on three publicly available datas-ets,ABILENE,GEANT,and CERNET,and the experimental results showed that the proposed STCFusion had significant effects.
network traffic predictionself-attention mechanismspatial-temporal features
NETL
NSTL
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