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An improved fruit fly algorithm-unscented Kalman filter-echo state network method for time series prediction of the network traffic data with noises

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Bibliographic Details
Title: An improved fruit fly algorithm-unscented Kalman filter-echo state network method for time series prediction of the network traffic data with noises
Authors: Han, Ying, Jing, Yuanwei, Dimirovski, Georgi M.
Publisher Information: Sage Publications Ltd
Publication Year: 2020
Collection: Doğuş University Institutional Repository (DSpace@Dogus) / Doğuş Üniversitesi Akademik Arşiv Sistemi
Subject Terms: Time series prediction, network traffic, echo state network, unscented Kalman filter, fruit fly optimization algorithm
Description: With the complexity of the network system rapidly increasing, network traffic prediction has great significance for the safety pre-warning of the network load, network management and control, and improvement of the quality of the network service. In this paper, the time series analysis is used for the network traffic prediction, and a prediction method combined with an optimized unscented Kalman filter (UKF) by an improved fruit fly algorithm (IFOA) and echo state network (ESN) is proposed, which is named by IFOA-UKF-ESN. The researches mainly solve the problem that the prediction accuracy might be greatly affected by the actual network traffic data with unknown and time-varying noises. UKF is used to train the best state vector (formed by spectral radius, scale of the reservoir, scale of the input units and connectivity rate) of ESN; and the proposed IFOA algorithm is proposed to optimize the weights of the predicted state value and the covariance in UKF, which makes UKF have adaptive ability for unknown and time-varying noise. Three actual network traffic data sets with different Gaussian white noise distributions are constructed for experiments, and the experimental results show that the proposed prediction method makes an average improvement by reducing at least 20.60%, 43.23% and 41.85% of RMSE, at least 23.66%, 52.38% and 47.50% of MAE, and at least 23.58%, 52.10% and 47.28% of MAPE, which verify the effectiveness of the proposed method. ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61773108] ; The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors are grateful for financial support from the National Natural Science Foundation of China (Grant No.61773108).
Document Type: article in journal/newspaper
Language: English
Relation: Transactions Of The Institute Of Measurement And Control; Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı; https://doi.org/10.1177/0142331219888366; https://hdl.handle.net/11376/3666; 42; 1281; 1293; WOS:000508077600001; Q3; Q2
DOI: 10.1177/0142331219888366
Availability: https://hdl.handle.net/11376/3666
https://doi.org/10.1177/0142331219888366
Rights: info:eu-repo/semantics/closedAccess
Accession Number: edsbas.D6910402
Database: BASE
Description
Abstract:With the complexity of the network system rapidly increasing, network traffic prediction has great significance for the safety pre-warning of the network load, network management and control, and improvement of the quality of the network service. In this paper, the time series analysis is used for the network traffic prediction, and a prediction method combined with an optimized unscented Kalman filter (UKF) by an improved fruit fly algorithm (IFOA) and echo state network (ESN) is proposed, which is named by IFOA-UKF-ESN. The researches mainly solve the problem that the prediction accuracy might be greatly affected by the actual network traffic data with unknown and time-varying noises. UKF is used to train the best state vector (formed by spectral radius, scale of the reservoir, scale of the input units and connectivity rate) of ESN; and the proposed IFOA algorithm is proposed to optimize the weights of the predicted state value and the covariance in UKF, which makes UKF have adaptive ability for unknown and time-varying noise. Three actual network traffic data sets with different Gaussian white noise distributions are constructed for experiments, and the experimental results show that the proposed prediction method makes an average improvement by reducing at least 20.60%, 43.23% and 41.85% of RMSE, at least 23.66%, 52.38% and 47.50% of MAE, and at least 23.58%, 52.10% and 47.28% of MAPE, which verify the effectiveness of the proposed method. ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61773108] ; The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors are grateful for financial support from the National Natural Science Foundation of China (Grant No.61773108).
DOI:10.1177/0142331219888366