System and method for cellular network prediction model analysis

A computing device, comprising:

• a memory configured to store computer instructions;
• and
• a processor system configured to execute the computer instructions to:
• select a plurality of possible network prediction models to predict traffic loads on target nodes in a cellular network;
• select a plurality of target requirements in which to compare the plurality of possible network prediction models for predicting the traffic loads on the target nodes in the cellular network;
• for each corresponding possible network prediction model of the plurality of possible network prediction models, score each corresponding target requirement of the plurality of target requirements for the corresponding possible network prediction model;
• select a first set of target nodes in the cellular network;
• for the first set of target nodes:
• select a first rank for the plurality of target requirements in which to assess the plurality of possible network prediction models with respect to the first set of target nodes;
• for each corresponding possible network prediction model of the plurality of possible network prediction models, calculate a first total score for the corresponding possible network prediction model based on the scores and the first rank of each corresponding target requirement for the corresponding possible network prediction model for the first set of target nodes;
• determine a first optimum network prediction model from the plurality of possible network prediction models for the first set of target nodes based on the first total score of each of the plurality of possible network prediction models;
• and
• employ the first optimum network prediction model to predict the traffic loads on the first set of target nodes in the cellular network;
• select a second set of target nodes in the cellular network, wherein the second set of target nodes is different from the first set of target nodes;
• and
• for the second set of target nodes:
• select a second rank for the plurality of target requirements in which to assess the plurality of possible network prediction models with respect to the second set of target nodes;
• for each corresponding possible network prediction model of the plurality of possible network prediction models, calculate a second total score for the corresponding possible network prediction model based on the scores and the second rank of each corresponding target requirement for the corresponding possible network prediction model for the second set of target nodes;
• determine a second optimum network prediction model from the plurality of possible network prediction models for the second set of target nodes based on the second total score of each of the plurality of possible network prediction models;
• and
• employ the second optimum network prediction model to predict the traffic loads on the second set of target nodes in the cellular network.

A non-transitory computer-readable storage medium that stores instructions that, when executed by a processor in a computing system, cause the processor to perform actions, the actions comprising:

• for each corresponding possible network prediction model of a plurality of possible network prediction models, scoring each corresponding target requirement of a plurality of target requirements for the corresponding possible network prediction model;
• for a first set of target nodes in a cellular network:
• ranking the plurality of target requirements in which to assess the plurality of possible network prediction models for predicting traffic loads on the first set of target nodes;
• for each corresponding possible network prediction model of the plurality of possible network prediction models, calculating a first total score for the corresponding possible network prediction model based on the scores and the ranking of each corresponding target requirement for the corresponding possible network prediction model for the first set of target nodes;
• and
• employing a first optimum network prediction model from the plurality of possible network prediction models to predict the traffic loads on the first set of target nodes in the cellular network, the first optimum network prediction model having a highest first total score;
• and
• for a second set of target nodes in the cellular network, the second set of target nodes being different from the first set of target nodes:
• re-ranking the plurality of target requirements in which to assess the plurality of possible network prediction models for predicting traffic loads on the second set of target nodes;
• for each corresponding possible network prediction model of the plurality of possible network prediction models, calculating a second total score for the corresponding possible network prediction model based on the scores and the re-ranking of each corresponding target requirement for the corresponding possible network prediction model for the second set of target nodes;
• and
• employing a second optimum network prediction model from the plurality of possible network prediction models to predict the traffic loads on the second set of target nodes in the cellular network, the second optimum network prediction model having a highest second total score.