I'm currently facing a Machine Learning problem and I've reached a point where I need some help to proceed.
I have various time series of positional (
z) data tracked by sensors. I've developed some more features. For example, I rasterized the whole 3D space and calculated a
cell_z for every time step. The time series itself have variable lengths.
My goal is to build a model which classifies every time step with the labels
1 (binary classification based on past and future values). Therefore I have a lot of training time series where the labels are already set.
One thing which could be very problematic is that there are very few
1's labels in the data (for example only 3 of 800 samples are labeled with
It would be great if someone can help me in the right direction because there are too many possible problems:
- Wrong hyperparameters
- Incorrect model
- Too few
1's labels, but I think that's not a big problem because I only need the model to suggests the right time steps. So I would only use the peaks of the output.
- Bad or too less training data
- Bad features
I appreciate any help and tips.