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We study anomaly detection and introduce an algorithm that processes variablelength, irregularly sampled sequences or sequences with missing values. Ouralgorithm is fully unsupervised, however, can be readily extended to supervisedor semisupervised cases when the anomaly labels are present as remarkedthroughout the paper. Our approach uses the Long Short Term Memory (LSTM)networks in order to extract temporal features and find the most relevantfeature vectors for anomaly detection. We incorporate the sampling timeinformation to our model by modulating the standard LSTM model with timemodulation gates. After obtaining the most relevant features from the LSTM, welabel the sequences using a Support Vector Data Descriptor (SVDD) model. Weintroduce a loss function and then jointly optimize the feature extraction andsequence processing mechanisms in an end-to-end manner. Through this jointoptimization, the LSTM extracts the most relevant features for anomalydetection later to be used in the SVDD, hence completely removes the need forfeature selection by expert knowledge. Furthermore, we provide a trainingalgorithm for the online setup, where we optimize our model parameters withindividual sequences as the new data arrives. Finally, on real-life datasets,we show that our model significantly outperforms the standard approaches thanksto its combination of LSTM with SVDD and joint optimization.

Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks