A Multiscale Bezier-Representation for Time Series That Supports Elastic Matching
Abstract
Common time series similarity measures that operate on the full series (like Euclidean distance or Dynamic Time Warping DTW) do not correspond well to the visual similarity as perceived by a human. Based on the interval tree of scale, we propose a multiscale Bezier representation of time series, that supports the definition of elastic similarity measures that overcome this problem. With this representation the matching can be performed efficiently as similarity is measured segment-wise rather than element-wise (as with DTW). We effectively restrict the set of warping paths considered by DTW and the results do not only correspond better to the analysts intuition but improve the accuracy in the standard 1NN time series classification.
Cite
Text
Höppner and Sobek. "A Multiscale Bezier-Representation for Time Series That Supports Elastic Matching." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71246-8_28Markdown
[Höppner and Sobek. "A Multiscale Bezier-Representation for Time Series That Supports Elastic Matching." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/hoppner2017ecmlpkdd-multiscale/) doi:10.1007/978-3-319-71246-8_28BibTeX
@inproceedings{hoppner2017ecmlpkdd-multiscale,
title = {{A Multiscale Bezier-Representation for Time Series That Supports Elastic Matching}},
author = {Höppner, Frank and Sobek, Tobias},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2017},
pages = {461-477},
doi = {10.1007/978-3-319-71246-8_28},
url = {https://mlanthology.org/ecmlpkdd/2017/hoppner2017ecmlpkdd-multiscale/}
}