The short-term network traffic prediction based on the ITCN algorithm

Chengsheng Pan; Yufu Wang; Li Yang

doi:10.1117/12.2640871

2 December 2022 The short-term network traffic prediction based on the ITCN algorithm

Chengsheng Pan, Yufu Wang, Li Yang

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 122881I (2022) https://doi.org/10.1117/12.2640871
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

The network traffic prediction is the basic premise of tasks such as dynamic planning and congestion control. However, due to the burstiness, volatility, and non-stationarity of the short-term network traffic, the prediction model is required to possess high timeliness and accuracy so that it is very difficult to predict. In order to solve the above-mentioned problems, an Improved Temporal Convolutional Network (ITCN) algorithm is proposed. This algorithm introduces a historical influence coefficient to improve the TCN algorithm, which heightens the efficiency of the TCN in processing traffic time series and simultaneously enhances the ability of the TCN algorithm to capture temporal features. The simulation experiments indicate as follows: in contrast to the comparison algorithm, the accuracy of the ITCN algorithm is improved by 60.99%; the training speed is improved by 94.52%, and the algorithm prediction speed is improved by 72.76%; the ITCN algorithm can meet the requirements of the accuracy and the timeliness for the short-term traffic prediction tasks.

Citation Download Citation

Chengsheng Pan, Yufu Wang, and Li Yang "The short-term network traffic prediction based on the ITCN algorithm", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 122881I (2 December 2022); https://doi.org/10.1117/12.2640871

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