Method of load forecasting via attentive knowledge transfer, and an apparatus for the same

An electronic device comprising:

• at least one memory storing computer-readable instructions;
• and
• at least one processor configured to execute the computer-readable instructions to:
• obtain source data sets and a target data set that have been collected from a plurality of source base stations and a target base station, respectively;
• among a plurality of source machine learning models configured to predict a communication traffic load of the plurality of source base stations, select at least one machine learn source model that has a traffic load prediction performance higher than that of a target machine learning model configured to predict a communication traffic load of the target base station through a negative transfer analysis on the plurality of source base stations;
• obtain model weights to be applied to the target machine learning model and the selected at least one source machine learning model via an attention neural network that is jointly trained with the target machine learning model and the selected source machine learning models;
• obtain a load forecasting model for the target base station by combining the target machine learning model and the selected at least one source machine learning model according to the model weights;
• and
• predict a future communication traffic load of the target base station based on the load forecasting model.

A non-transitory computer-readable storage medium having recorded thereon instructions to be executed by at least one processor to perform a method of forecasting a future load, the method comprising:

• obtaining source data sets and a target data set that have been collected from a plurality of source base stations and a target base station, respectively;
• among a plurality of source machine learning models configured to predict a communication traffic load of the plurality of source base stations, selecting at least one machine learn source model that has a traffic load prediction performance higher than that of a target machine learning model configured to predict a communication traffic load of the target base station through a negative transfer analysis on the plurality of source base stations;
• obtaining model weights to be applied to the target machine learning model and the selected at least one source machine learning model via an attention neural network that is jointly trained with the target machine learning model and the selected source machine learning models;
• obtaining a load forecasting model for the target base station by combining the target machine learning model and the selected at least one source machine learning model according to the model weights;
• and
• predicting a future communication traffic load of the target base station based on the load forecasting model.