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I am building an anomaly detection system of event sequence data (transactions). For each timestep, a transaction can be in any of 76 different stages.

My dataset is therefore a 3D array of size(m,t,N).

m is number of samples (~40 000), t = the length of the longest transaction (I am zero padding all other sequences of shorter length), N is the number of possible events at each timestep. $x_{m,t,N} = \{0,1\}$ where $x_{v,u,w} = 1$ if transaction v is in event w at timestep u otherwise it's 0.

So far I have created an LSTM Variational Auto Encoder in Python with Keras and doing outlier detection by applying Local Outlifer Factor on the latent representations of the transactions. It's based on this paper https://arxiv.org/abs/1906.10896 and following various blogs such as https://blog.keras.io/building-autoencoders-in-keras.html.

I would like to compare the results with other techniques. I was wondering if anyone has any other suggestions on algorithms and techniques for anomaly detection in unlabeled event sequence data?

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You might be interested in sequential association analysis/sequential rule mining or time-aware attention (note the latter paper is written for the electronic medical record context, but the ideas should be transferable)

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