Abstract
Prediction of consumption has several applications in industry, including to support strategic decisions, market offering, and value proposition. In telecommunications industry, it can also be used in network resources management and in guaranteeing quality of service to users. But in order to make good predictions, one should choose the algorithm that is best fitted to the considered time series and also configures the parameters correctly. In this chapter, we discuss the use of time series forecasting algorithms over telecommunications data. We evaluate the use of Auto-Regressive Integrated Moving Average (ARIMA), Prophet (launched by Facebook in 2017), and two neural network algorithms: Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM). We ran those algorithms over real data about Internet data consumption and mobile phone cards recharges, in order to forecast time periods of distinct sizes. Forecasted values were qualified in terms of Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Obtained results show that ARIMA is the algorithm that is best suited to most cases.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
StatsModels Statistics in Python. http://www.statsmodels.org/devel/generated/statsmodels.tsa.arima_model.ARIMA.html. Accessed 20 Mar 2018
Prophet Forecasting at scale. https://facebook.github.io/prophet/. Accessed 16 Apr 2018
Neural network models (supervised). https://scikit-learn.org/stable/modules/neural_networks_supervised.html. Accessed 23 Nov 2018
Keras: The Python Deep Learning library. https://keras.io/layers/recurrent/. Accessed 24 Nov 2018
Branco, S.T., De Sampaio, R.J.B.: A new artificial neural networks forecast model in telecommunications. In: Computing and Computational Techniques in Sciences, pp. 70–77. WSEAS Press (2008)
Xingjian, S., Chen, Z., Wang, H., Yeung, D., Wong, W., Woo, W.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810 (2015)
Malhotra, P., Vig, L., Shroff, G., Agarwal, P.: Long short term memory networks for anomaly detection in time series. In: 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015, pp. 89–94 (2015)
Wang, M., Wang, Y., Wang, X., Wei, Z.: Forecast and analyze the telecom income based on ARIMA model. Open Cybern. Syst. J. 9(1), 2559–2564 (2015). https://doi.org/10.2174/1874110X01509012559
Is prophet really better than ARIMA for forecasting time series data. https://blog.exploratory.io/is-prophet-better-than-arima-for-forecasting-time-series-fa9ae08a5851. Accessed 16 May 2018
Pinho, A., Silva, H., Furtado, P.: Experimental comparison and tuning of time series prediction for telecom analysis. In: Proceedings of International Conference on Time Series and Forecasting, ITISE, vol. 2, pp. 586–597 (2018)
A comprehensive beginner’s guide to create a Time Series Forecast. https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/. Accessed 15 Mar 2018
MATH6011: Forecasting. https://www.southampton.ac.uk/~abz1e14/papers/Forecasting.pdf. Accessed 22 Mar 2018
Taylor, S.J., Letham, B.: Forecasting at scale. The American Statistician (just-accepted) (2017). Accessed 16 Apr 2018
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2011)
Azzouni, A., Pujolle, G.: A long short-term memory recurrent neural network framework for network traffic matrix prediction (2017). arXiv preprint arXiv:1705.05690. Accessed 1 Dec 2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Pinho, A., Costa, R., Silva, H., Furtado, P. (2019). Comparing Time Series Prediction Approaches for Telecom Analysis. In: Valenzuela, O., Rojas, F., Pomares, H., Rojas, I. (eds) Theory and Applications of Time Series Analysis. ITISE 2018. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-26036-1_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-26036-1_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26035-4
Online ISBN: 978-3-030-26036-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)