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

A non-transitory computer-readable medium including computer-executable instructions that, when executed by a computerized mobile wireless data network performance parameter forecasting system, facilitate rendering a forecast for a mobile wireless network performance parameter by performing a 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 the raw data point set, the correcting comprising the sub-steps 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, and
• a 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 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, and
• applying 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.