A comprehensive review on hybrid network traffic prediction model
Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the...
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| Format: | Article |
| Language: | English English |
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Yogyakarta: Institute of Advanced Engineering and Science (IAES)
2021
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| Online Access: | https://eprints.ums.edu.my/id/eprint/28915/1/A%20comprehensive%20review%20on%20hybrid%20network%20traffic%20prediction%20model%20FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/28915/2/A%20comprehensive%20review%20on%20hybrid%20network%20traffic%20prediction%20model.pdf |
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| author | Jinmei Shi Leau, Yu-Beng Kun Li Joe Henry Obit |
| author_facet | Jinmei Shi Leau, Yu-Beng Kun Li Joe Henry Obit |
| author_sort | Jinmei Shi |
| collection | UMS |
| description | Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes with a summary of the strengths and limitations of existing hybrid network prediction models which use optimization and decomposition techniques, respectively. These two techniques have been identified as major contributing factors in constructing a more accurate and fast response hybrid network traffic prediction. |
| first_indexed | 2024-03-06T03:08:12Z |
| format | Article |
| id | ums.eprints-28915 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:08:12Z |
| publishDate | 2021 |
| publisher | Yogyakarta: Institute of Advanced Engineering and Science (IAES) |
| record_format | dspace |
| spelling | ums.eprints-289152021-07-11T13:53:00Z https://eprints.ums.edu.my/id/eprint/28915/ A comprehensive review on hybrid network traffic prediction model Jinmei Shi Leau, Yu-Beng Kun Li Joe Henry Obit Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes with a summary of the strengths and limitations of existing hybrid network prediction models which use optimization and decomposition techniques, respectively. These two techniques have been identified as major contributing factors in constructing a more accurate and fast response hybrid network traffic prediction. Yogyakarta: Institute of Advanced Engineering and Science (IAES) 2021 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/28915/1/A%20comprehensive%20review%20on%20hybrid%20network%20traffic%20prediction%20model%20FULL%20TEXT.pdf text en https://eprints.ums.edu.my/id/eprint/28915/2/A%20comprehensive%20review%20on%20hybrid%20network%20traffic%20prediction%20model.pdf Jinmei Shi and Leau, Yu-Beng and Kun Li and Joe Henry Obit (2021) A comprehensive review on hybrid network traffic prediction model. International Journal of Electrical and Computer Engineering (IJECE), 11 (2). pp. 1450-1459. ISSN 2722-2578 https://d1wqtxts1xzle7.cloudfront.net/66125562/57_22989_EM_10sep_22apr_N-with-cover-page-v2.pdf?Expires=1625792813&Signature=SP3MrnSmI5p-x08CNIQNI99O8yJtzQLv1eJtbPeIVI-2PidF1CF4TFnDae4FRsNEh5X526Qw5rpI8RIDJ3cHL49WnajX5KYgMnvjnQk2vcAvNrsAAiLcmzQ6a8KVgKp7MyyM9mGt4ZMOJv5Zzol1nqmGghUpyIDwE51R7CNbdpEYr0eaI5xVD9T-IlifJ5TGJ0fsZZgAulCHSVM97g8wgtuKC1ghfKWtgblHqdYWvOMWRutc4PF7neJ2JGQeHb7i8Riclij2fKNDfxhIlZ8c~R0eNsWDkvI23TaZNHopynFjUrM0kZaNTeIkRvOZyMf5Z5fFUEBbg-VuuaZjOpwOSg__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA https://doi.org/10.11591/ijece.v11i2.pp1450-1459 https://doi.org/10.11591/ijece.v11i2.pp1450-1459 |
| spellingShingle | Jinmei Shi Leau, Yu-Beng Kun Li Joe Henry Obit A comprehensive review on hybrid network traffic prediction model |
| title | A comprehensive review on hybrid network traffic prediction model |
| title_full | A comprehensive review on hybrid network traffic prediction model |
| title_fullStr | A comprehensive review on hybrid network traffic prediction model |
| title_full_unstemmed | A comprehensive review on hybrid network traffic prediction model |
| title_short | A comprehensive review on hybrid network traffic prediction model |
| title_sort | comprehensive review on hybrid network traffic prediction model |
| url | https://eprints.ums.edu.my/id/eprint/28915/1/A%20comprehensive%20review%20on%20hybrid%20network%20traffic%20prediction%20model%20FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/28915/2/A%20comprehensive%20review%20on%20hybrid%20network%20traffic%20prediction%20model.pdf |
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