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Cellular Traffic Prediction and Classification: A Comparative Evaluation of LSTM and ARIMA

  • Amin AzariEmail author
  • Panagiotis Papapetrou
  • Stojan Denic
  • Gunnar Peters
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11828)

Abstract

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.

Keywords

Statistical learning Machine learning LSTM ARIMA Cellular traffic Predictive network management 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Amin Azari
    • 1
    Email author
  • Panagiotis Papapetrou
    • 1
  • Stojan Denic
    • 2
  • Gunnar Peters
    • 2
  1. 1.Department of Computer and Systems SciencesStockholm UniversityStockholmSweden
  2. 2.HuaweiStockholmSweden

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