Abstract
The traffic flow prediction of cellular network requires low complexity and high accuracy, which is difficult to meet using the existing methods. In this paper, we propose an long short-term memory (LSTM) network based traffic flow prediction in which we consider temporal correlations inherently and nonlinear characteristics of cellular network traffic flow data. We use Back Propagation Through Time (BPTT) to train the LSTM network and evaluate the model using mean square error (MSE) and mean absolute error (MAE). Simulation results show that the proposed LSTM network based traffic flow prediction for cellular network is superior to the stacked autoencoder network based algorithm.
The financial support of the program of Key Industry Innovation Chain of Shaanxi Province, China (2017ZDCXL-GY-04-02), of the program of Xi’an Science and Technology Plan (201805029YD7CG13(5)), Shaanxi, China, of National S&T Major Project (No. 2016ZX03001022-003), China, and of Key R&D Program - The Industry Project of Shaanxi (Grant No. 2018GY-017) are gratefully acknowledged.
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References
Feng, H., Shu, Y.: Study on network traffic prediction techniques. In: International Conference on Wireless Communications, NETWORKING and Mobile Computing, pp. 1041–1044 (2005)
Voort, M.V.D., Dougherty, M., Watson, S.: Combining kohonen maps with arima time series models to forecast traffic flow. Transp. Res. Part C Emerg. Technol. 4(5), 307–318 (1996)
Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. J. Transp. Eng. 129(6), 664–672 (2003)
Katsaros, D., Manolopoulos, Y.: Prediction in wireless networks by Markov chains. Wirel. Commun. IEEE 16(2), 56–64 (2009)
Alarcon-Aquino, V., Barria, J.A.: Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction. IEEE Trans. Syst. Man Cybern. Part C 36(2), 208–220 (2006)
Babiarz, R., Bedo, J.-S.: Internet traffic mid-term forecasting: a pragmatic approach using statistical analysis tools. In: Boavida, F., Plagemann, T., Stiller, B., Westphal, C., Monteiro, E. (eds.) NETWORKING 2006. LNCS, vol. 3976, pp. 110–122. Springer, Heidelberg (2006). https://doi.org/10.1007/11753810_10
Chabaa, S., Zeroual, A., Antari, J.: Identification and prediction of internet traffic using artificial neural networks. J. Intell. Learn. Syst. Appl. 2(3), 147–155 (2010)
Cortez, P., Rio, M., Rocha, M., et al.: Multi-scale Internet traffic forecasting using neural networks and time series methods. Expert Syst. 29(2), 143–155 (2012)
Liu, X., Fang, X., Qin, Z., et al.: A short-term forecasting algorithm for network traffic based on chaos theory and SVM. J. Netw. Syst. Manag. 19(4), 427–447 (2011)
Wang, J., Wang, J., Zeng, M., et al.: Prediction of internet traffic based on Elman neural network. In: Control and Decision Conference, CCDC 2009, Chinese, pp. 1248–1252 (2009)
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (2002)
Wang, H., Hu, D.: Comparison of SVM and LS-SVM for regression. In: International Conference on Neural Networks and Brain, pp. 279–283 (2005)
Chen, Y., Yang, B., Meng, Q.: Small-time scale network traffic prediction based on flexible neural tree. Appl. Soft Comput. 12(1), 274–279 (2012)
Lv, Y., Duan, Y., Kang, W., et al.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)
Oliveira, T.P., Barbar, J.S., Soares, A.S.: Multilayer perceptron and stacked autoencoder for internet traffic prediction. In: Hsu, C.-H., Shi, X., Salapura, V. (eds.) NPC 2014. LNCS, vol. 8707, pp. 61–71. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44917-2_6
Huang, W., Song, G., Hong, H., et al.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191–2201 (2014)
Zhuo, Q., Li, Q., Yan, H., Qi, Y.: Long short-term memory neural network for network traffic prediction. In: International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 1–6 (2017)
Gers, F.A., Schmidhuber, J.: Recurrent nets that time and count. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, vol. 3, pp. 189–194 (2000)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kang, D., Lv, Y., Chen, Y.Y.: Short-term traffic flow prediction with LSTM recurrent neural network. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1–6 (2017)
Vidnerova, P., Neruda, R.: Evolving keras architectures for sensor data analysis. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 109–112 (2017)
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Cao, S., Liu, W. (2019). LSTM Network Based Traffic Flow Prediction for Cellular Networks. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-32216-8_63
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DOI: https://doi.org/10.1007/978-3-030-32216-8_63
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