Network Traffic Time Series Performance Analysis Using Statistical Methods
Abstract
This paper presents an approach for a network traffic characterization by using statistical techniques. These techniques are obtained using the decomposition, winter’s exponential smoothing and autoregressive integrated moving average (ARIMA). In this paper, decomposition and winter’s exponential smoothing techniques were used additive and multiplicative model. Then, ARIMA based-on Box-Jenkins methodology. The results of ARIMA (1,0,2) was shown the best model that can be used to the internet network traffic forecasting.
Full Text:
PDFReferences
Box, G.E.P., G.M. Jenkins, and G.C. Reinsel, Time Series Analysis Forecasting and Control Fourth Edition. 2008, Copyright © 2008 by John Wiley & Sons, Inc. All rights reserved. crossref
Wei, W.W.S., Time Series Analysis Univariate and Multivariate Methods Second Edition. 2006, Pearson Education, Inc. All rights reserved. crossref
Santos, A.C.F., et al., Network traffic characterization based on Time Series Analysis and Computational Intelligence. Journal of Computational Interdisciplinary Sciences, 2011. 2(3): p. pp. 197-205. crossref
Brockwell, P.J. and R.A. Davis, Introduction to Time Series and Forecasting Second Edition, G. Casella, Editor. 2002, © 2002, 1996 Springer-Verlag New York, Inc. crossref
Li, C. and T.-W. Chiang, Complex Neurofuzzy ARIMA Forecasting—A New Approach Using Complex Fuzzy Sets. IEEE Transactions on Fuzzy Systems, 2013. 21(NO. 3, JUNE 2013). crossref
Bernacki, J. and G. Kołaczek, Anomaly Detection in Network Traffic Using Selected Methods of Time Series Analysis. I. J. Computer Network and Information Security, 2015. 9: p. 10-18. crossref
Sermpinis, G., et al., Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination and time-varying leverage. Decision Support Systems, 2012. 54, (2012): p. 316–329. crossref
Khashei, M. and M. Bijari, An artificial neural network (p, d,q) model for timeseries forecasting. Expert Systems with Applications, 2010. 37 (2010): p. 479–489. crossref
Khashei, M. and M. Bijari, A new class of hybrid models for time series forecasting. Expert Systems with Applications, 2012. 39(2012): p. 4344–4357. crossref
Gomes, G.S.d.S. and T.B. Ludermir, Optimization of the weights and asymmetric activation function family of neural network for time series forecasting. Expert Systems with Applications, 2013. 40(2013): p. 6438–6446. crossref
Haviluddin and R. Alfred, Forecasting Network Activities Using ARIMA Method. Journal of Advances in Computer Networks (JACN), 2014. 2, (3) September 2014: p. 173-179. crossref
DOI: http://dx.doi.org/10.17977/um018v1i12018p1-7
Refbacks
- There are currently no refbacks.
Copyright (c) 2017 Knowledge Engineering and Data Science
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.