@inproceedings{a16fd614cacb40de8d868c339daf8c60,
title = "On-Line Dynamic Time Warping for Streaming Time Series",
abstract = "Dynamic Time Warping is a well-known measure of dissimilarity between time series. Due to its flexibility to deal with non-linear distortions along the time axis, this measure has been widely utilized in machine learning models for this particular kind of data. Nowadays, the proliferation of streaming data sources has ignited the interest and attention of the scientific community around on-line learning models. In this work, we naturally adapt Dynamic Time Warping to the on-line learning setting. Specifically, we propose a novel on-line measure of dissimilarity for streaming time series which combines a warp constraint and a weighted memory mechanism to simplify the time series alignment and adapt to non-stationary data intervals along time. Computer simulations are analyzed and discussed so as to shed light on the performance and complexity of the proposed measure.",
keywords = "Dynamic Time Warping, On-line learning, Time series",
author = "Izaskun Oregi and Aritz P{\'e}rez and {Del Ser}, Javier and Lozano, {Jos{\'e} A.}",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017 ; Conference date: 18-09-2017 Through 22-09-2017",
year = "2017",
doi = "10.1007/978-3-319-71246-8_36",
language = "English",
isbn = "9783319712451",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "591--605",
editor = "Michelangelo Ceci and Jaakko Hollmen and Ljupco Todorovski and Celine Vens and Saso Dzeroski",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings",
address = "Germany",
}