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DeepAnT - A Deep Learning Approach forUnsupervised Anomaly Detectionin Time Series

·1 min

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.