(untitled) (Advances in Systems Science and Applications, 2017)
The problem of anomaly detection arises in many practical applications. Currently it is highly important to be able to detect outliers in data streams, as recent years have seen a rapid growth in the amount of such data. Only a few techniques are applicable to real-time data and even fewer could provide an interpretable anomaly score. Probabilistic interpretation of the anomaly score could allow an analyst to choose the anomaly threshold based on the desired false alarm rate, which is highly important in a number of real-life applications. We propose a modification of the EXPoSE algorithm for anomaly detection in time series data, which produces a probabilistic score of abnormality. The proposed algorithm is developed within the framework of conformal anomaly detection and utilizes the expected similarity as a measure of non-conformity.