Novel anomaly detection approach for telecommunication network proactive performance monitoring
Abstract The mode of telecommunication network management is changing from “network oriented” to “subscriber oriented”. Aimed at enhancing subscribers’ feeling, proactive performance monitoring (PPM) can enable a fast fault correction by detecting anomalies designating performance degradation. In th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yu, Yanhua [verfasserIn] Wang, Jun [verfasserIn] Zhan, Xiaosu [verfasserIn] Song, Junde [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2009 |
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| Schlagwörter: |
proactive performance monitoring (PPM) |
|---|
| Übergeordnetes Werk: |
Enthalten in: Frontiers of electrical and electronic engineering in China - Berlin : Heidelberg : Springer, 2006, 4(2009), 3 vom: 10. Juni, Seite 307-312 |
|---|---|
| Übergeordnetes Werk: |
volume:4 ; year:2009 ; number:3 ; day:10 ; month:06 ; pages:307-312 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11460-009-0051-9 |
|---|
| Katalog-ID: |
SPR019848838 |
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| 520 | |a Abstract The mode of telecommunication network management is changing from “network oriented” to “subscriber oriented”. Aimed at enhancing subscribers’ feeling, proactive performance monitoring (PPM) can enable a fast fault correction by detecting anomalies designating performance degradation. In this paper, a novel anomaly detection approach is the proposed taking advantage of time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average (ARIMA). Furthermore, under the assumption that the training residual is a white noise process following a normal distribution, the associated confidence interval of prediction can be figured out under any given confidence degree 1 - α by constructing random variables satisfying t distribution. Experimental results verify the method’s effectiveness. | ||
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10.1007/s11460-009-0051-9 doi (DE-627)SPR019848838 (SPR)s11460-009-0051-9-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Yu, Yanhua verfasserin aut Novel anomaly detection approach for telecommunication network proactive performance monitoring 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The mode of telecommunication network management is changing from “network oriented” to “subscriber oriented”. Aimed at enhancing subscribers’ feeling, proactive performance monitoring (PPM) can enable a fast fault correction by detecting anomalies designating performance degradation. In this paper, a novel anomaly detection approach is the proposed taking advantage of time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average (ARIMA). Furthermore, under the assumption that the training residual is a white noise process following a normal distribution, the associated confidence interval of prediction can be figured out under any given confidence degree 1 - α by constructing random variables satisfying t distribution. Experimental results verify the method’s effectiveness. proactive performance monitoring (PPM) (dpeaa)DE-He213 anomaly detection (dpeaa)DE-He213 time series prediction (dpeaa)DE-He213 autoregressive integrated moving average (ARIMA) (dpeaa)DE-He213 white noise (dpeaa)DE-He213 confidence interval (dpeaa)DE-He213 Wang, Jun verfasserin aut Zhan, Xiaosu verfasserin aut Song, Junde verfasserin aut Enthalten in Frontiers of electrical and electronic engineering in China Berlin : Heidelberg : Springer, 2006 4(2009), 3 vom: 10. Juni, Seite 307-312 (DE-627)510464297 (DE-600)2230606-7 1673-3584 nnns volume:4 year:2009 number:3 day:10 month:06 pages:307-312 https://dx.doi.org/10.1007/s11460-009-0051-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_40 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_110 GBV_ILN_120 GBV_ILN_161 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2018 GBV_ILN_2190 53.00 ASE AR 4 2009 3 10 06 307-312 |
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10.1007/s11460-009-0051-9 doi (DE-627)SPR019848838 (SPR)s11460-009-0051-9-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Yu, Yanhua verfasserin aut Novel anomaly detection approach for telecommunication network proactive performance monitoring 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The mode of telecommunication network management is changing from “network oriented” to “subscriber oriented”. Aimed at enhancing subscribers’ feeling, proactive performance monitoring (PPM) can enable a fast fault correction by detecting anomalies designating performance degradation. In this paper, a novel anomaly detection approach is the proposed taking advantage of time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average (ARIMA). Furthermore, under the assumption that the training residual is a white noise process following a normal distribution, the associated confidence interval of prediction can be figured out under any given confidence degree 1 - α by constructing random variables satisfying t distribution. Experimental results verify the method’s effectiveness. proactive performance monitoring (PPM) (dpeaa)DE-He213 anomaly detection (dpeaa)DE-He213 time series prediction (dpeaa)DE-He213 autoregressive integrated moving average (ARIMA) (dpeaa)DE-He213 white noise (dpeaa)DE-He213 confidence interval (dpeaa)DE-He213 Wang, Jun verfasserin aut Zhan, Xiaosu verfasserin aut Song, Junde verfasserin aut Enthalten in Frontiers of electrical and electronic engineering in China Berlin : Heidelberg : Springer, 2006 4(2009), 3 vom: 10. Juni, Seite 307-312 (DE-627)510464297 (DE-600)2230606-7 1673-3584 nnns volume:4 year:2009 number:3 day:10 month:06 pages:307-312 https://dx.doi.org/10.1007/s11460-009-0051-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_40 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_110 GBV_ILN_120 GBV_ILN_161 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2018 GBV_ILN_2190 53.00 ASE AR 4 2009 3 10 06 307-312 |
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10.1007/s11460-009-0051-9 doi (DE-627)SPR019848838 (SPR)s11460-009-0051-9-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Yu, Yanhua verfasserin aut Novel anomaly detection approach for telecommunication network proactive performance monitoring 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The mode of telecommunication network management is changing from “network oriented” to “subscriber oriented”. Aimed at enhancing subscribers’ feeling, proactive performance monitoring (PPM) can enable a fast fault correction by detecting anomalies designating performance degradation. In this paper, a novel anomaly detection approach is the proposed taking advantage of time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average (ARIMA). Furthermore, under the assumption that the training residual is a white noise process following a normal distribution, the associated confidence interval of prediction can be figured out under any given confidence degree 1 - α by constructing random variables satisfying t distribution. Experimental results verify the method’s effectiveness. proactive performance monitoring (PPM) (dpeaa)DE-He213 anomaly detection (dpeaa)DE-He213 time series prediction (dpeaa)DE-He213 autoregressive integrated moving average (ARIMA) (dpeaa)DE-He213 white noise (dpeaa)DE-He213 confidence interval (dpeaa)DE-He213 Wang, Jun verfasserin aut Zhan, Xiaosu verfasserin aut Song, Junde verfasserin aut Enthalten in Frontiers of electrical and electronic engineering in China Berlin : Heidelberg : Springer, 2006 4(2009), 3 vom: 10. Juni, Seite 307-312 (DE-627)510464297 (DE-600)2230606-7 1673-3584 nnns volume:4 year:2009 number:3 day:10 month:06 pages:307-312 https://dx.doi.org/10.1007/s11460-009-0051-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_40 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_110 GBV_ILN_120 GBV_ILN_161 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2018 GBV_ILN_2190 53.00 ASE AR 4 2009 3 10 06 307-312 |
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10.1007/s11460-009-0051-9 doi (DE-627)SPR019848838 (SPR)s11460-009-0051-9-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Yu, Yanhua verfasserin aut Novel anomaly detection approach for telecommunication network proactive performance monitoring 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The mode of telecommunication network management is changing from “network oriented” to “subscriber oriented”. Aimed at enhancing subscribers’ feeling, proactive performance monitoring (PPM) can enable a fast fault correction by detecting anomalies designating performance degradation. In this paper, a novel anomaly detection approach is the proposed taking advantage of time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average (ARIMA). Furthermore, under the assumption that the training residual is a white noise process following a normal distribution, the associated confidence interval of prediction can be figured out under any given confidence degree 1 - α by constructing random variables satisfying t distribution. Experimental results verify the method’s effectiveness. proactive performance monitoring (PPM) (dpeaa)DE-He213 anomaly detection (dpeaa)DE-He213 time series prediction (dpeaa)DE-He213 autoregressive integrated moving average (ARIMA) (dpeaa)DE-He213 white noise (dpeaa)DE-He213 confidence interval (dpeaa)DE-He213 Wang, Jun verfasserin aut Zhan, Xiaosu verfasserin aut Song, Junde verfasserin aut Enthalten in Frontiers of electrical and electronic engineering in China Berlin : Heidelberg : Springer, 2006 4(2009), 3 vom: 10. Juni, Seite 307-312 (DE-627)510464297 (DE-600)2230606-7 1673-3584 nnns volume:4 year:2009 number:3 day:10 month:06 pages:307-312 https://dx.doi.org/10.1007/s11460-009-0051-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_40 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_110 GBV_ILN_120 GBV_ILN_161 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2018 GBV_ILN_2190 53.00 ASE AR 4 2009 3 10 06 307-312 |
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10.1007/s11460-009-0051-9 doi (DE-627)SPR019848838 (SPR)s11460-009-0051-9-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Yu, Yanhua verfasserin aut Novel anomaly detection approach for telecommunication network proactive performance monitoring 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The mode of telecommunication network management is changing from “network oriented” to “subscriber oriented”. Aimed at enhancing subscribers’ feeling, proactive performance monitoring (PPM) can enable a fast fault correction by detecting anomalies designating performance degradation. In this paper, a novel anomaly detection approach is the proposed taking advantage of time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average (ARIMA). Furthermore, under the assumption that the training residual is a white noise process following a normal distribution, the associated confidence interval of prediction can be figured out under any given confidence degree 1 - α by constructing random variables satisfying t distribution. Experimental results verify the method’s effectiveness. proactive performance monitoring (PPM) (dpeaa)DE-He213 anomaly detection (dpeaa)DE-He213 time series prediction (dpeaa)DE-He213 autoregressive integrated moving average (ARIMA) (dpeaa)DE-He213 white noise (dpeaa)DE-He213 confidence interval (dpeaa)DE-He213 Wang, Jun verfasserin aut Zhan, Xiaosu verfasserin aut Song, Junde verfasserin aut Enthalten in Frontiers of electrical and electronic engineering in China Berlin : Heidelberg : Springer, 2006 4(2009), 3 vom: 10. Juni, Seite 307-312 (DE-627)510464297 (DE-600)2230606-7 1673-3584 nnns volume:4 year:2009 number:3 day:10 month:06 pages:307-312 https://dx.doi.org/10.1007/s11460-009-0051-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_40 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_110 GBV_ILN_120 GBV_ILN_161 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2018 GBV_ILN_2190 53.00 ASE AR 4 2009 3 10 06 307-312 |
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Novel anomaly detection approach for telecommunication network proactive performance monitoring |
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Novel anomaly detection approach for telecommunication network proactive performance monitoring |
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Yu, Yanhua |
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Frontiers of electrical and electronic engineering in China |
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Yu, Yanhua Wang, Jun Zhan, Xiaosu Song, Junde |
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novel anomaly detection approach for telecommunication network proactive performance monitoring |
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Novel anomaly detection approach for telecommunication network proactive performance monitoring |
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Abstract The mode of telecommunication network management is changing from “network oriented” to “subscriber oriented”. Aimed at enhancing subscribers’ feeling, proactive performance monitoring (PPM) can enable a fast fault correction by detecting anomalies designating performance degradation. In this paper, a novel anomaly detection approach is the proposed taking advantage of time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average (ARIMA). Furthermore, under the assumption that the training residual is a white noise process following a normal distribution, the associated confidence interval of prediction can be figured out under any given confidence degree 1 - α by constructing random variables satisfying t distribution. Experimental results verify the method’s effectiveness. |
| abstractGer |
Abstract The mode of telecommunication network management is changing from “network oriented” to “subscriber oriented”. Aimed at enhancing subscribers’ feeling, proactive performance monitoring (PPM) can enable a fast fault correction by detecting anomalies designating performance degradation. In this paper, a novel anomaly detection approach is the proposed taking advantage of time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average (ARIMA). Furthermore, under the assumption that the training residual is a white noise process following a normal distribution, the associated confidence interval of prediction can be figured out under any given confidence degree 1 - α by constructing random variables satisfying t distribution. Experimental results verify the method’s effectiveness. |
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Abstract The mode of telecommunication network management is changing from “network oriented” to “subscriber oriented”. Aimed at enhancing subscribers’ feeling, proactive performance monitoring (PPM) can enable a fast fault correction by detecting anomalies designating performance degradation. In this paper, a novel anomaly detection approach is the proposed taking advantage of time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average (ARIMA). Furthermore, under the assumption that the training residual is a white noise process following a normal distribution, the associated confidence interval of prediction can be figured out under any given confidence degree 1 - α by constructing random variables satisfying t distribution. Experimental results verify the method’s effectiveness. |
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Novel anomaly detection approach for telecommunication network proactive performance monitoring |
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