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Cellular Traffic Prediction and Classification: A Comparative Evaluation of LSTM and ARIMA
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2019 (English)In: Discovery Science: Proceedings / [ed] Petra Kralj Novak, Tomislav, Šmuc, Sašo Džeroski, Springer, 2019, p. 129-144Conference paper, Published paper (Refereed)
Abstract [en]

Prediction of user traffic in cellular networks has attracted profound attention for improving the reliability and efficiency of network resource utilization. In this paper, we study the problem of cellular network traffic prediction and classification by employing standard machine learning and statistical learning time series prediction methods, including long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA), respectively. We present an extensive experimental evaluation of the designed tools over a real network traffic dataset. Within this analysis, we explore the impact of different parameters on the effectiveness of the predictions. We further extend our analysis to the problem of network traffic classification and prediction of traffic bursts. The results, on the one hand, demonstrate the superior performance of LSTM over ARIMA in general, especially when the length of the training dataset is large enough and its granularity is fine enough. On the other hand, the results shed light onto the circumstances in which, ARIMA performs close to the optimal with lower complexity.

Place, publisher, year, edition, pages
Springer, 2019. p. 129-144
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11828
Keywords [en]
Statistical learning, Machine learning, LSTM, ARIMA, Cellular traffic, Predictive network management
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-177140DOI: 10.1007/978-3-030-33778-0_11ISBN: 978-3-030-33777-3 (print)ISBN: 978-3-030-33778-0 (electronic)OAI: oai:DiVA.org:su-177140DiVA, id: diva2:1379860
Conference
22nd International Conference, DS 2019, Split, Croatia, October 28–30, 2019
Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2019-12-17Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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More languages
Output format
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