Outlier Removal for the Reliable Condition Monitoring of Telecommunication Services

Fahrnberger, Günter

doi:10.1109/pdcat46702.2019.00052

Cited by 7 publications

(13 citation statements)

References 15 publications

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“…The sections about related work in both ancestors of this scholarly piece cite abundant literature about outlier exclusion respectively threshold evaluation [2,3].…”

Section: Related Workmentioning

confidence: 99%

“…In 1950, John Edward Walsh published some nonparametric statistical tests of whether the largest observations of a set are too large or too small [3,5]. Nonparametric statistics covers techniques that do not depend on any particular probability distribution in order to function appropriately.…”

Section: Outlier Removalmentioning

confidence: 99%

“…That is why such tests can be readily applied without incipient distribution determination, which makes them more lightweight. In 1953, Walsh felt obliged to issue an amendment to Theorem 4 of his original publication [3,6].…”

Section: Outlier Removalmentioning

confidence: 99%

“…Two scholarly pieces especially deal with the CM of telecommunication services with time-varying load characteristic [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…The second ameliorates its ancestor with an outlier removal technique and a further automatic threshold calculation method, which do their job for any statistical distribution of the KPI history [3]. The latter also places a guideline at disposal how to supervise new services.…”

Section: Introductionmentioning

confidence: 99%

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Threshold Pair Selection for the Reliable Condition Monitoring of Telecommunication Services

Fahrnberger

2021

Innovations for Community Services

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A service provider usually measures the conditions of its service(s) via Key Performance Indicators (KPIs) and lets a Condition Monitoring System (CMS) continuously oversee them. A CMS additionally notifies the responsible workforce of abnormal KPI values. Telecommunication services often show time-varying load characteristic, i.e. their amplitudes of use fluctuate time-dependently. On account of that, a CMS has to cope the Condition Monitoring (CM) of such telecommunication services by dint of fluctuating thresholds. A couple of existing disquisitions have pioneered the calculation of dynamical thresholds to this end. Aside from the normal condition, contemporary CMSs can typically classify into the two measurable abnormal states warning and critical by way of two collaborating thresholds. Up to now, nobody has publicized a mechanism to extract the two ideal thresholds from an array that comprises at least three available ones. This contribution closes this gap by introducing three threshold extraction strategies, scrutinizing their reliability, and recommending ideas for further ones.

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“…The sections about related work in both ancestors of this scholarly piece cite abundant literature about outlier exclusion respectively threshold evaluation [2,3].…”

Section: Related Workmentioning

confidence: 99%

Section: Outlier Removalmentioning

confidence: 99%

Section: Outlier Removalmentioning

confidence: 99%

“…Two scholarly pieces especially deal with the CM of telecommunication services with time-varying load characteristic [2,3].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations