As shown in the attached figure, I am trying to use machine learning to identify segments of erreneous data by looking at a timeseries of raw data. I created an output variable that classifies the data as 'good' or 'bad' based on how the raw data differs from the clean data. I tried inputting a single variable along with neighbors in time and found little success with the Classification Learner App. How might I be able to use machine learning/other methods to identify these erroneous data segments from just the raw data? I can clearly see a jump in the timeseries at the end of the bad data sections (where the sensors were cleaned), so I feel like an algorithm should also be able to pick that up at least.