Network-Wide Traffic Anomaly Detection and Localization Based on Robust Multivariate Probabilistic Calibration Model

<div class="floats-full-page floats-full-page-other"><h2>Table 1</h2><div class="floats-full-page-caption">Execution time of RMPCM anomaly detection.</div><div class="floats-full-page-content"><table class="table-group" id="tab1"><tr><td><table class="table"><tr><td class="thead-hr" colspan="5"><hr /></td></tr><tr class="thead"><td align="left">Simulation experiment</td><td align="center" colspan="2">Testbed experiment</td><td align="center" colspan="2">Real network data analysis</td></tr><tr class="thead"><td align="left">Complete data</td><td align="center">Complete data</td><td align="center">Missing data (mean)</td><td align="center">Complete data</td><td align="center">Missing data (mean)</td></tr><tr><td class="thead-hr" colspan="5"><hr /></td></tr><tr><td align="left">2.15 s</td><td align="center">1.94 s</td><td align="center">3.56 s</td><td align="center">2.53 s</td><td align="center">4.86 s</td></tr><tr class="table-tr"><td colspan="5"><hr class="tbody-hr" /></td></tr></table></td></tr></table></div></div>

Table 1 | Network-Wide Traffic Anomaly Detection and Localization Based on Robust Multivariate Probabilistic Calibration Model

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Table 1: Network-Wide Traffic Anomaly Detection and Localization Based on Robust Multivariate Probabilistic Calibration Model

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Table 1

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