How to set the Reconstruction error threshold for anomaly detection using autoencoders?
If one considers prediction of anomalous status as binary classification (i.e., if reconstruction error &lt; threshold, classify as normal, else classify as anomalous), one can find the threshold that maximizes some appropriate metric of classification performance (e.g., F-beta) by optimizing the metric over a suitable validation set containing normal and anomalous data. See Malhotra et al., 2016 for an example of how to do this for time series.