# DeepAnT - A Deep Learning Approach forUnsupervised Anomaly Detectionin Time Series

DeepAnT: A Deep Learning Approach forUnsupervised Anomaly Detectionin Time Series:

- It’s a long paper but, at it’s core, it presents a straightforward idea.
- Use CNN as a forecasting model to predict data for a given timestamp.
- Calculate an anomaly score by comparing the prediction against the observed value and call the time-series anomalous (for that timestamp) if the score is higher than some threshold.

- I found two problems with their proposal of calculating anomaly scores:
- It doesn’t take variance into account and only uses absolute difference. If the latter is fixed, shouldn’t anomaly score be inversely proportional to variance?
- How do I interpret that score in business terminology?

- They used Mean Absolute Error as the loss function for the model. Why not Mean Squared Error?
- The idea of choosing time-series specific thresholds would be tricky but the paper doesn’t clarify how to automate that at scale.