UNDERTOW: Multi-Level Segmentation of Real-Valued Time Series
Abstract
The discovery of meaningful change points, finding segments, in both categorical and real-value data time series is a well-studied problem. Prior segmentation algorithms and tasks operate under overly restrictive assumptions (e.g., a priori knowledge of the number of segments, trivial inputs) and in singular domains (e.g., finding common regions in images, speaker change detection). We introduce a domain-independent algorithm, UNDERTOW, which discovers segment boundaries in real-valued time series and constructs hierarchies of segments to form macro segments.
Cite
Text
Armstrong and Oates. "UNDERTOW: Multi-Level Segmentation of Real-Valued Time Series." AAAI Conference on Artificial Intelligence, 2007.Markdown
[Armstrong and Oates. "UNDERTOW: Multi-Level Segmentation of Real-Valued Time Series." AAAI Conference on Artificial Intelligence, 2007.](https://mlanthology.org/aaai/2007/armstrong2007aaai-undertow/)BibTeX
@inproceedings{armstrong2007aaai-undertow,
title = {{UNDERTOW: Multi-Level Segmentation of Real-Valued Time Series}},
author = {Armstrong, Tom and Oates, Tim},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2007},
pages = {1842-1843},
url = {https://mlanthology.org/aaai/2007/armstrong2007aaai-undertow/}
}