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I need suggestions for my project and would be glad if you would give me a hand.

I have a dataset of frames obtained from the old-school game DOOM. Each frame in the dataset has the following columns:

time, frame buffer size in bytes, game stage (0: Exploration, 1: Combat)

Once collecting consecutive frames to calculate instant bitrate (kbps) for each time instance, I apply Exponential Moving Average on bitrate to smooth and here below the data I end up with:

Bitrate Data

I need to train a model by using this data and in the end, I should be able to classify game stages correctly. Currently, I have two approaches:

  1. Calculating the average bitrate of each stage, and examining the % change from the former stage to the latter one (avg_latter - avg_former / avg_former * 100) in different baseline bitrate ranges: (W=9, 18, 35 below indicates Exponential Moving Average window lengths)

Result for transitions from Exploration -> Combat

Result for transitions from Combat -> Exploration

  1. Calculating the bitrate variance in each stage change moment by looking 'N' frames back and forward: Bitrate Variance Calculation

Results of transitions from Exploration -> Combat and viceversa

According to the results above, I wonder which methodology would make more sense for game stage classification. Any idea is appreciated. Thanks!

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