Is my dataset unlearnable, or is my LSTM model not smart enough? I have time-series data obtained from a video. The data is composed of bitrate and corresponding label pairs for each timestamp:

The distribution over the first 30 seconds is as follows:

I have built an LSTM model for this dataset to be able to classify the labels based on the bitrate. However, it seems that my model is not able to learn. Validation accuracy starts from approximately 0.3 (makes sense, since I have 2 classes (log2 = 0.3)) and it does not improve.
Do you have any idea about this? Is it normal considering this sample data distribution, or is something might be wrong with my model? Thanks!
An LSTM uses the preceding sequence of bitrates to produce a label. If the label can be predicted from those bitrates, then your problem is learnable with an LSTM.
From your comment: The label is the &quot;stage of the video&quot;: Running, walking, etc. The question is if this can be derived from the bitrate? Walking with a busy background might yield a higher bitrate than running. Judging your example by eye, I suspect it cannot. You need to get more features from the video than just the bitrate, I think.