Multi-step network traffic prediction using echo state network with a selective error compensation strategy

Author:

Han Ying12ORCID,Jing Yuanwei1,Dimirovski Georgi M3,Zhang Li4

Affiliation:

1. Northeastern University, China

2. Liaoning Technical University, China

3. SS Cyril and Methodius University, Macedonia

4. CCTEG Shenyang Research Institute, China

Abstract

Communication networks grow exponentially in this globalization era; thus, the network traffic modelling and prediction plays a crucial role in network management and security warning. Solely, the multi-step network traffic prediction may involve greater errors hence worsening prediction performance. To overcome this problem, an optimized echo state network model with selective error compensation is proposed. In the optimized echo state network-based multi-step prediction model, an improved fruit–fly optimization algorithm based on cloud model (named LVCMFOA) is used to select optimum values of four key parameters of the model. The proposed LVCMFOA algorithm uses the levy-flight function to redefine the generation of the fruit–fly population, which can randomly change the search radius and help getting out of a possible local optimal solution and prevent local optimum. To reduce the calculation time but improve the prediction accuracy simultaneously, a sophisticated selective error compensation strategy employing the variable sliding window technology is proposed so as to avoid the error accumulation problem in the multi-step prediction. The effectiveness of the proposed method is verified by applying it to Henon mapping chaotic series, Mackey–Glass chaotic series and two public network traffic data sets all known in the literature.

Funder

National Natural Science Foundation of China

Publisher

SAGE Publications

Subject

Instrumentation

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1. Network Traffic Identification Based on Improved EM Algorithm;IEEE Access;2024

2. STDA-Meta: A Meta-Learning Framework for Few-Shot Traffic Prediction;2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS);2023-12-17

3. Sequence prediction with different dimensions based on two novel deep echo state network models;Transactions of the Institute of Measurement and Control;2023-10-21

4. Leaky echo state network based on methane topology applied to time series prediction;IET Control Theory & Applications;2023-10-09

5. Grey Wolf Optimization–Based Deep Echo State Network for Time Series Prediction;Frontiers in Energy Research;2022-03-11

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