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Detecting and processing anomalous parameter data points by a mobile wireless data network forecasting system

  • US 10,531,251 B2
  • Filed: 10/22/2012
  • Issued: 01/07/2020
  • Est. Priority Date: 10/22/2012
  • Status: Active Grant
First Claim
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1. A method for rendering, by a computerized mobile wireless data network performance parameter forecasting system, a forecast for a mobile wireless network performance parameter, the method comprising the steps of:

  • acquiring, via a communications network infrastructure for the mobile wireless network, a raw data point set containing a series of data point values for the mobile wireless network parameter;

    correcting, by the computerized mobile wireless data network performance parameter forecasting system, the raw data point set, the correcting comprising a set of sub-steps, executed by the computerized forecasting system, of;

    performing a first linear regression on the raw data point set to render an initial best fit line,calculating a current series of data point residuals based upon;

    a current best fit line, the current best fit line being in a first instance the initial best fit line, anda current series of data point values, the current series of data point values being in a first instance the raw data point set,decomposing the current series of data point residuals into a current set of seasonal data point residual subsets,performing a boxplot analysis on the current set of seasonal data point residual subsets to identify individual anomalous point residuals to render a current set of anomalous data point residuals,correcting the current set of anomalous data point residuals to render a corrected data point residual set, andapplying the corrected data point residual set to the current best fit line to render a current corrected data point set; and

    applying the current corrected data point set to a forecast model to render a forecast for the mobile wireless network performance parameter.

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