Intelligent Hybrid Model to Enhance Time Series Models for Predicting Network Traffic

作     者：Aldhyani, Theyazn H. H. Alrasheedi, Melfi Alqarni, Ahmed Abdullah Alzahrani, Mohammed Y. Bamhdi, Alwi M. 

作者机构：King Faisal Univ Community Coll Abqaiq Al Hasa 31982 Saudi Arabia King Faisal Univ Dept Quantitat Methods Sch Business Al Hasa 31982 Saudi Arabia Albaha Univ Dept Comp Sci & Informat Technol Al Baha 65431 Saudi Arabia Umm Al Qura Univ Coll Comp Mecca 24231 Saudi Arabia 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2020年第8卷

页      面：130431-130451页

核心收录：

基　　金：King Faisal University 

主　　题：Telecommunication traffic Predictive models Autoregressive processes Time series analysis Computational modeling Prediction algorithms Machine learning Machine intelligence soft computing machine learning network traffic deep learning algorithm 

摘      要：Network traffic analysis and predictions have become vital for monitoring networks. Network prediction is the process of capturing network traffic and examining it deeply to decide what is the occurrence in the network. The accuracy of analysis and estimation of network traffic are increasingly becoming significant in achieving guaranteed Quality of Service (QoS) in the network. The main aim of the presented research is to propose a new methodology to improve network traffic prediction by using sequence mining. The significance of this important topic lies in the urge to contribute to solving the research problem in network traffic prediction intelligently. We propose an integrated model that combines clustering with existing series models to enhance prediction the network traffic. Clustering granules are obtained using fuzzy c-means to analyze the network data for improving the existing time series. The novelty of the proposed research has used the clustering approach to handle the ambiguity from the entire network data for enhancing the existing time series models. Furthermore, we have suggested using the weighted exponential smoothing model as preprocessing stages for increasing the reliability of the proposed model. In this research paper, machine intelligence proposed to predict network traffic. The machine intelligence is working as pre-processing for enhancing the existing time series models. The machine intelligence combines non-crisp Fuzzy-C-Means (FCM) clustering and the weight exponential method for improving deep learning Long Short-Time Memory(LSTM)and Adaptive Neuro-Fuzzy Inference System (ANFIS)time series models. The ANFIS and LSMT time series models are applied to predict network traffic. Two real network traffic traces were conducted to test the proposed time series models. The empirical results of proposed to enhanced LSTM 97.95% and enhanced ANFIS model is R = 96.78% for cellular traffic data, with respect to the correlation indicator. It is obse