Abstract
Based on time-series detection algorithm, this paper puts forward a new analysis method for identify Network Element (NE) hitches. Aiming at specific characteristics of the NE, this paper propose a model which consider seasonal timing characteristics and impact of current data from recent data. Considering of multi-dimensional characteristics of NE, a density-based discovery algorithm is introduced into the modeling process. Experiments on the actual data coming from operates demonstrate the effectiveness and accuracy of the proposed methods.
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
Breunig, M.: LOF: identifying density-based local outliers. ACM SIGMOD Rec. 29(2), 93–104 (2000)
Kriegel, H.P., Schubert, E., Zimek, A.: LoOP:local outlier probabilities. In: ACM Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China, DBLP, pp. 1649–1652, November 2009
Lazarevic, A., Kumar, V.: Feature bagging for outlier detection. In: Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 157–166. ACM (2005)
Schubert, E., Wojdanowski, R., Zimek, A., et al.: On evaluation of outlier rankings and outlier scores. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 1047–1058 (2012)
Guan, H., Li, Q., Yan, Z., et al.: SLOF: identify density-based local outliers in big data. In: Web Information System and Application Conference, pp. 61–66. IEEE (2015)
Booth, E.G., Mount, J.F., Viers, J.H.: Hydrologic variability of the cosumnes river floodplain. San Francisco Estuary Watershed Sci. 4(2), 1–19 (2006)
Domańska, J., Domański, A., Czachórski, T.: Fluid flow analysis of RED algorithm with modified weighted moving average. In: Dudin, A., Klimenok, V., Tsarenkov, G., Dudin, S. (eds.) BWWQT 2013. CCIS, vol. 356, pp. 50–58. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35980-4_7
Glabadanidis, P.: Market timing with moving averages. Int. Rev. Finance 15(3), 387–425 (2015)
Yan, X., Su, X., World Scientific: Linear Regression Analysis [Electronic Resource]: Theory and Computing (2009)
Gergonne, J.D.: The application of the method of least squares to the interpolation of sequences. Historia Mathematica 1(4), 439–447 (1974)
Stigler, S.M.: Gergonne’s 1815 paper on the design and analysis of polynomial regression experiments. Historia Mathematica 1(4), 431–439 (1974)
Brown, R.G.: Exponential Smoothing for Predicting Demand, p. 15. Arthur D. Little Inc., Cambridge (1956)
Brown, R.G.: Smoothing, forecasting and prediction of discrete time series. J. R. Stat. Soc. 127(2) (1964)
Mills, T.C.: Time Series Techniques for Economists. Cambridge University Press, Cambridge (1990)
Jenkins, G.M.: Autoregressive-Integrated Moving Average (ARIMA) Models. Encyclopedia of Statistical Sciences. Wiley, New York (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Zhang, D., Man, Y., Ren, L. (2018). A Composite Anomaly Detection Method for Identifying Network Element Hitches. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2017. Lecture Notes in Computer Science(), vol 10745. Springer, Cham. https://doi.org/10.1007/978-3-319-74521-3_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-74521-3_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74520-6
Online ISBN: 978-3-319-74521-3
eBook Packages: Computer ScienceComputer Science (R0)