Predicting Fault In Telecommunication Network: Lessons Learned
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2020 by IJETT Journal|
|Year of Publication : 2020|
|Authors : Mohd Izhan Mohd Yusoff
|DOI : 10.14445/22315381/IJETT-V68I12P228|
MLA Style: Mohd Izhan Mohd Yusoff. Predicting Fault In Telecommunication Network: Lessons Learned International Journal of Engineering Trends and Technology 68.12(2020):171-181.
APA Style:Mohd Izhan Mohd Yusoff. Predicting Fault In Telecommunication Network: Lessons Learned International Journal of Engineering Trends and Technology, 68(12), 171-181.
Faults in the telecommunication network has attracted a large amount of interest from researchers and practitioners who have introduced, for example, algorithms to extract faulty signatures from noisy historical event data; rules and decision tree data mining classifiers to upgrade fault detection and handling, and cluster head selection algorithms to address the failure under uncertain situations. This article discusses lessons learned from studying and applying statistical methods or techniques to predict faults in the telecommunication networks (which is our main objective or target). Cochrane Orcutt`s approach or procedure was identified for having properties that we believe would fulfill or achieve the above objective or target. The challenges we faced were: real data collected from the device show seasonal trends (meaning the device is faulty periodically), and we showed, by using simulated seasonal data, the Cochrane Orcutt approach or procedure failed to get the desired results. We proposed the Cochrane Orcutt adjusted approach or procedure where we showed, by using simulated seasonal data, the said adjusted approach or procedure managed to get the desired results. We also suggest future research recommendations using advanced methods (especially when met with the ideal case or scenario).
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Telecommunication network, Faults, Cochrane-Orcutt adjusted approach or procedure, Multiple regression, Autocorrelation function (ACF) plot, Partial Autocorrelation function (PACF) plot, Correlation matrix.