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Analysis of telecom service operation behavior with time series. (English) Zbl 07176677

Summary: 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.

MSC:

90Bxx Operations research and management science
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[1] Alvarez, Fm; Troncoso, A.; Riquelme, Jc; Ruiz, Jsa, Energy time series forecasting based on pattern sequence similarity, IEEE Trans Knowl Data Eng, 23, 8, 1230-1243 (2011) · doi:10.1109/TKDE.2010.227
[2] Alwan, Lc; Roberts, Hv, Time-series modeling for statistical process control, J Bus Econ Stat, 6, 1, 87-95 (1988)
[3] Box, Ge; Jenkins, Gm; Reinsel, Gc; Ljung, Gm, Time series analysis: forecasting and control (2015), New York: Wiley, New York
[4] Brodersen, Kh; Gallusser, F.; Koehler, J.; Remy, N.; Scott, Sl, Inferring causal impact using bayesian structural time-series models, Ann Appl Stat, 9, 1, 247-274 (2015) · Zbl 1454.62473 · doi:10.1214/14-AOAS788
[5] Cleveland, Rb; Cleveland, Ws; Terpenning, I., Stl: a seasonal-trend decomposition procedure based on loess, J Off Stat, 6, 1, 3 (1990)
[6] Frye, J.; Gordon, Rj, Government intervention in the inflation process: the econometrics of “Self-Inflicted Wounds”, Am Econ Rev, 71, 2, 288-294 (1981)
[7] Marcellino, M.; Stock, Jh; Watson, Mw, A comparison of direct and iterated multistep ar methods for forecasting macroeconomic time series, J Econom, 135, 1, 499-526 (2006) · Zbl 1418.62513 · doi:10.1016/j.jeconom.2005.07.020
[8] Medina, I.; Montaner, D.; Tárraga, J.; Dopazo, J., Prophet, a web-based tool for class prediction using microarray data, Bioinformatics, 23, 3, 390-391 (2006) · doi:10.1093/bioinformatics/btl602
[9] Pridemore, Wa; Chamlin, Mb, A time-series analysis of the impact of heavy drinking on homicide and suicide mortality in Russia, 1956-2002, Addiction, 101, 12, 1719-1729 (2006) · doi:10.1111/j.1360-0443.2006.01631.x
[10] Sani MF, van der Aalst W, Bolt A, García-Algarra J (2017) Subgroup discovery in process mining. In: International conference on business information systems. Springer, Berlin, pp 237-252
[11] Taylor, Jw, A comparison of univariate time series methods for forecasting intraday arrivals at a call center, Manag Sci, 54, 2, 253-265 (2008) · Zbl 1232.90214 · doi:10.1287/mnsc.1070.0786
[12] Taylor, Sj, Modelling financial time series (2008), Singapore: World Scientific, Singapore · Zbl 1146.91033
[13] Valipour, M., Long-term runoff study using sarima and arima models in the United States, Meteorol Appl, 22, 3, 592-598 (2015) · doi:10.1002/met.1491
[14] Van der Aalst WMP (2011) Process discovery: an introduction. In: Process mining. Springer, Berlin, pp 125-156
[15] 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) · doi:10.2174/1874110X01509012559
[16] Ye X (2010) The application of arima model in chinese mobile user prediction. In: 2010 IEEE international conference on granular computing (GrC). IEEE, pp 586-591
[17] Zivot E, Wang J (2006) Vector autoregressive models for multivariate time series. In: Modeling financial time series with \(S-PLUS^{\textregistered }\), Springer, New York, NY, pp 385-429 · Zbl 1092.91067
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