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Prediction of network traffic is very important function of any network. Traffic prediction is important to ensure good system efficiency and ensure service quality of IoT applications, as it relies primarily on congestion management, admission control, allocation of bandwidth to the system, and the identification of anomalies. In this paper, a complete overview of IoT traffic forecasting model using classic time series and artificial neural network is presented. For prediction of IoT traffic, real network traces are used. Prediction models are evaluated using MAE, RMSE, and <jats:italic>R<\/jats:italic>\u2010squared values. The experimental results indicate that LSTM\u2010 and FNN\u2010based predictive models are highly sensitive and can therefore be used to provide better performance as a timing sequence forecast model than the conventional traffic prediction techniques.<\/jats:p>","DOI":"10.1155\/2021\/5366222","type":"journal-article","created":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T19:50:42Z","timestamp":1627933842000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7152-9719","authenticated-orcid":false,"given":"Shilpa P.","family":"Khedkar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7279-5522","authenticated-orcid":false,"given":"R. 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