Time and Again: - Time Series Mining via Recurrence Quantification Analysis
Abstract
Recurrence quantification analysis (RQA) was developed in order to quantify differently appearing recurrence plots (RPs) based on their small-scale structures, which generally indicate the number and duration of recurrences in a dynamical system. Although RQA measures are traditionally employed in analyzing complex systems and identifying transitions, recent work has shown that they can also be used for pairwise dissimilarity comparisons of time series. We explain why RQA is not only a modern method for nonlinear data analysis but also is a very promising technique for various time series mining tasks.
Cite
Text
Spiegel and Marwan. "Time and Again: - Time Series Mining via Recurrence Quantification Analysis." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46131-1_30Markdown
[Spiegel and Marwan. "Time and Again: - Time Series Mining via Recurrence Quantification Analysis." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/spiegel2016ecmlpkdd-time/) doi:10.1007/978-3-319-46131-1_30BibTeX
@inproceedings{spiegel2016ecmlpkdd-time,
title = {{Time and Again: - Time Series Mining via Recurrence Quantification Analysis}},
author = {Spiegel, Stephan and Marwan, Norbert},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {258-262},
doi = {10.1007/978-3-319-46131-1_30},
url = {https://mlanthology.org/ecmlpkdd/2016/spiegel2016ecmlpkdd-time/}
}