Abstract: As the premise and foundation of advertisement traffic selling and distribution, effective IPTV advertisement traffic prediction not only reduces the operation cost, but also improves the intelligent level of new media advertisement traffic management. In order to further improve the accuracy of new media advertisement traffic prediction, this paper proposes a new media advertisement traffic prediction method based on the hybrid prediction framework of decomposition-optimisation-integration, which is a hybrid model of gated recurrent unit neural network and deep confidence network improved by capsule swarm optimisation algorithm. Firstly, according to the principle of system construction, paper analyses the influencing factors and construct a complete new media advertisement traffic prediction index system; secondly, paper improves the optimisation process of the parameters of the deep confidence network and the gated recurrent unit network by using the quilt group optimisation algorithm, and put forward a new media advertisement traffic prediction method based on the decomposition-optimisation-integration framework; Finally, the proposed method is analysed using new media advertisement traffic data. The results show that the proposed method improves the accuracy of the prediction model and solves the problem of large prediction error in new media advertisement traffic prediction methods.

Ronghua Zhang, “DBN-GRU Fusion and Decomposition-Optimisation-Reconstruction Algorithm in Advertising Traffic Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151157

@article{Zhang2024,
title = {DBN-GRU Fusion and Decomposition-Optimisation-Reconstruction Algorithm in Advertising Traffic Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151157},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151157},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Ronghua Zhang}
}