| 1. |
- Azari, Amin, et al.
(författare)
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Cellular Traffic Prediction and Classification : A Comparative Evaluation of LSTM and ARIMA
- 2019
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Ingår i: Discovery Science. - Cham : Springer. - 9783030337773 - 9783030337780 ; , s. 129-144
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Konferensbidrag (refereegranskat)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.
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| 2. |
- Azari, Amin, et al.
(författare)
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User Traffic Prediction for Proactive Resource Management : Learning-Powered Approaches
- 2020
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Ingår i: IEEE Global Communications Conference (GLOBECOM). - : IEEE. - 9781728109626 - 9781728109633 ; , s. 1-6
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Konferensbidrag (refereegranskat)abstract
- Traffic prediction plays a vital role in efficient planning and usage of network resources in wireless networks. While traffic prediction in wired networks is an established field, there is a lack of research on the analysis of traffic in cellular networks, especially in a content-blind manner at the user level. Here, we shed light into this problem by designing traffic prediction tools that employ either statistical, rule-based, or deep machine learning methods. First, we present an extensive experimental evaluation of the designed tools over a real traffic dataset. Within this analysis, the impact of different parameters, such as length of prediction, feature set used in analyses, and granularity of data, on accuracy of prediction are investigated. Second, regarding the coupling observed between behavior of traffic and its generating application, we extend our analysis to the blind classification of applications generating the traffic based on the statistics of traffic arrival/departure. The results demonstrate presence of a threshold number of previous observations, beyond which, deep machine learning can outperform linear statistical learning, and before which, statistical learning outperforms deep learning approaches. Further analysis of this threshold value represents a strong coupling between this threshold, the length of future prediction, and the feature set in use. Finally, through a case study, we present how the experienced delay could be decreased by traffic arrival prediction.
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| 3. |
- Rebane, Jonathan, et al.
(författare)
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Seq2Seq RNNs and ARIMA models for Cryptocurrency Prediction : A Comparative Study
- 2018
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Ingår i: Proceedings of SIGKDD Workshop on Fintech (SIGKDD Fintech’18).
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Konferensbidrag (refereegranskat)abstract
- Cyrptocurrency price prediction has recently become an alluring topic, attracting massive media and investor interest. Traditional models, such as Autoregressive Integrated Moving Average models (ARIMA) and models with more modern popularity, such as Recurrent Neural Networks (RNN’s) can be considered candidates for such financial prediction problems, with RNN’s being capable of utilizing various endogenous and exogenous input sources. This study compares the model performance of ARIMA to that of a seq2seq recurrent deep multi-layer neural network (seq2seq) utilizing a varied selection of inputs types. The results demonstrate superior performance of seq2seq over ARIMA, for models generated throughout most of bitcoin price history, with additional data sources leading to better performance during less volatile price periods.
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