Unsupervised outlier detection for time series by entropy and dynamic time warping

In the last decade, outlier detection for temporal data has received much attention from data mining and machine learning communities. While other works have addressed this problem by two-way approaches (similarity and clustering), we propose in this paper an embedded technique dealing with both methods simultaneously. We reformulate the task of outlier detection as a weighted clustering problem based on entropy and dynamic time warping for time series. The outliers are then detected by an optimization problem of a new proposed cost function adapted to this kind of data. Finally, we provide some experimental results for validating our proposal and comparing it with other methods of detection.