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Here we use two tables to show the predictability of busy and idle duration data of video and audio services over noninterference and interference scenarios.
We can find that the maximum predictability was distributed from 51% to 72% for busy data and 56% to 69% for idle data under different environments.
It should be noted that data predictability just provides the level of possible prediction, but it has no any results related to specific methods to realize the up bound.
Therefore, how to improve prediction accuracy?
Here we propose one simple data categorization to increase the lower bound of predictability of partial data, and then can get better prediction accuracy
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IEEE/ICACT20210239 Slide.11
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