A Customized Approach to Anomalies Detection by using Autoencoders

When dealing with sensor's data, it's important to keep track of what it's really happening in the tracked environments since failures, interruptions and misreadings must be expected at any time. Especially with logging processes involving extremely voluminous reports, an automatic method to detect entries that are not following the normal distribution of data (i.e. anomalies) should be the ideal solution. In the presented work the task performed by the autoencoder is to generate a reproduction error, used as metric for the classification of a sample in one of two classes: anomalous or non-anomalous.