Analysis of telecom service operation behavior with time series

Operation of complex telecom services is a field that mixes technology, processes and teams. Despite the existence of detailed protocols and automation, the real behavior is hard to measure and predict. The human factor is a source of uncertainty, and this fact is of special relevance when facing stressful situations. Informal team working culture, time shifts or external stress are main sources of change. In this research we use time series analysis as a statistical proxy to detect this kind of drift in teams that solve network failures if three live services: IPTV, Cloud Infrastructure and IoT. This task known as incident management. This would provide not only a numerical evidence of the uncertainty in troubleshooting of digital services but also an assessment about the economic and operational impact of service releases. Changes in best fitting models may reflect different informal work cultures among the operation teams.

[1]  M. Valipour.  Long‐term runoff study using SARIMA and ARIMA models in the United States , 2015 .

[2]  Francisco Martinez Alvarez,et al.  Energy Time Series Forecasting Based on Pattern Sequence Similarity , 2011, IEEE Transactions on Knowledge and Data Engineering.

[3]  James W. Taylor,et al.  A Comparison of Univariate Time Series Methods for Forecasting Intraday Arrivals at a Call Center , 2008, Manag. Sci..

[4]  Xu Ye.  The Application of ARIMA Model in Chinese Mobile User Prediction , 2010, 2010 IEEE International Conference on Granular Computing.

[5]  Wil M. P. van der Aalst,et al.  Subgroup Discovery in Process Mining , 2017, BIS.

[6]  Irma J. Terpenning,et al.  STL : A Seasonal-Trend Decomposition Procedure Based on Loess , 1990 .

[7]  Wil M. P. van der Aalst,et al.  Process Discovery: An Introduction , 2011 .

[8]  Zhen Wei,et al.  Forecast and Analyze the Telecom Income based on ARIMA Model , 2015 .

[9]  Jon Frye,et al.  Government Intervention in the Inflation Process: The Econometrics of "Self-Inflicted Wounds" , 1980 .

[10]  Joaquín Dopazo,et al.  Prophet, a web-based tool for class prediction using microarray data , 2007, Bioinform..

[11]  Stephen L Taylor,et al.  Modelling Financial Time Series , 1987 .

[12]  Mitchell B. Chamlin,et al.  A time-series analysis of the impact of heavy drinking on homicide and suicide mortality in Russia, 1956-2002. , 2006, Addiction.

[13]  Steven L. Scott,et al.  Inferring causal impact using Bayesian structural time-series models , 2015, 1506.00356.

[14]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[15]  J. Stock,et al.  A Comparison of Direct and Iterated Multistep Ar Methods for Forecasting Macroeconomic Time Series , 2005 .

[16]  Layth C. Alwan,et al.  Time-Series Modeling for Statistical Process Control , 1988 .