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The distribution model of continuous busy/idle duration data of video/audio service over noninterference scenario and interference scenario has been researched. The fitting models can provide promising fitting accuracy.
The up and low bounds of predictability of busy/idle duration data has been evaluated and compared.
From the compared predictability, the prediction performances using an AR based predictor with data categorization has been proposed and evaluated. The results show that data categorization is an efficient way to improve the prediction accuracy of partial data.
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IEEE/ICACT20210239 Slide.16
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