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I just started my first project where I'm trying to find anomalies in the energy usage of a air conditioner. The only usable data I could obtain was the energy data for a few months. Since the energy usage was mostly discontinuous, I decided that it would be a good idea to compare the cumulative energy usage every day to get a good model.

I read that it would be relatively simpler if we could reduce contextual anomalies to point anomalies. Hence, I interpolated the available data to 30 second intervals so that I can compare the data for every time stamp over the period of months. However, what I did not think about was the fact that I would have to set a probability threshold below which the data point is an anomaly for every 30 seconds (2880 individual threshold values). Is there a way to automatically set the thresholds for all these time stamps? If not, what would be a good way to restart this given the data I have is very limited?

I also realise that it is not possible to get enough labelled data to calculate the F1 scores at every time stamp and gauge the optimal epsilon (threshold) using this.

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