This is the repository of the implementation of TASC as presented in the preprint submitted to the Scientific Reports.
Behavior consists of a series of repeating yet variable discrete motifs across various timescales. We introduce a framework for temporally aligned segmentation and clustering (TASC) of behavioral time series. TASC is designed to extract such motif recurrences in high temporal resolution. This framework operates iteratively in two steps: (1) embedding of time series segments and calculation of linearly aligned distances within the clustered space, and (2) recalculating of the clustered space after alignment. We evaluated TASC on semi-synthetic experimental and clinical datasets and it demonstrated enhanced segmentation performance. TASC may be applied to other domains where analysis of recurring time series patterns with high temporal precision is needed. Initial upload will contain original and ready to use code described in the paper. Future uploads should improve on the implementation and compatability. In development. This project is licensed under the terms of the GNU general license. See license.