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.s2i seq2seq lstm outlier detector Jan 8, 2019

Sequence-to-Sequence LSTM (seq2seq-LSTM) Outlier Detector


Anomaly or outlier detection has many applications, ranging from preventing credit card fraud to detecting computer network intrusions.

The implemented seq2seq outlier detector aims to predict anomalies in a sequence of input features. The model can be trained in an unsupervised or semi-supervised way, which is helpful since labeled training data is often scarce. The outlier detector predicts whether the input features represent normal behaviour or not, dependent on a threshold level set by the user.


The architecture of the seq2seq model is defined in and it is trained by running the script. The OutlierSeq2SeqLSTM class loads a pre-trained model and makes predictions on new data.

A detailed explanation of the implementation and usage of the seq2seq model as an outlier detector can be found in the seq2seq documentation.

Running on Seldon

An end-to-end example running a seq2seq outlier detector on GCP or Minikube using Seldon to identify anomalies in ECGs is available here.

Docker images to use the generic Mahalanobis outlier detector as a model or transformer can be found on Docker Hub:

A model docker image specific for the demo is also available: