Notebooks implementations about skab HOT 2 CLOSED

Hello, @RahimKh !
Thank you for your remarks! Cause we are still working on the methodology for algorithms evaluation, your comment is helpful.
We do see 3 possible ways of model fitting (fault-free train set selection):

1. (priority option) Using separate file (data/anomaly-free/anomaly-free.csv) with relatively long fault-free operating mode. Once trained and applied to all datasets. The problem here is connected with some troubles that appeared during the data collecting, making the fault-free dataset too different from most of the other datasets. It caused the recognition of the wrong patterns by the anomaly detection algorithms. We are currently working on collecting a proper fault-free dataset for model fitting in the future.
2. Use the beginning of one dataset as a fault-free mode. Once trained and applied to all datasets.
3. Use the beginning of each dataset as a fault-free mode. Trained and applied using every single dataset.

We have selected the 3rd way, for now, using the first 400 points of each dataset (approx 1/3 of the total number of points) as a train set. It is not entirely fair (doing so, we decrease the number of unknown points, making the problem easier to solve), but still, it is ok for a changepoint detection problem. As for the outlier detection problem: though generally, you are right saying "results need to be done on only data that is unknown to the model" for metrics (FAR, MAR, F1) calculation, it can still be an option. In this case, the results are just slightly overstated.

We definitely want to switch to the 1st way of model fitting. Probably we will switch to the 2nd way while the proper separate fault-free dataset is unavailable.

from skab.

The answer moved to the slides about the project.

from skab.