Data supporting the publication: Global Anomaly Detection using Feedforward Symmetrical Autoencoder Neuronal Network. Comparison with Other Methods in a Case Study using Real Industrial Data.

The research objective was to evaluate artificial intelligence-based methods for global anomaly detection in a drinking water treatment plant, where data was stored in a timeseries format. The Autoencoder neural network-based method proved superior to 5 other methods with which the study compared to, in terms of precision, recall and F1 score metrics.