An Order-invariant Time Series Distance Measure - Position on Recent Developments in Time Series Analysis

Stephan Spiegel, Sahin Albayrak

2012

Abstract

Although there has been substantial progress in time series analysis in recent years, time series distance measures still remain a topic of interest with a lot of potential for improvements. In this paper we introduce a novel Order Invariant Distance measure which is able to determine the (dis)similarity of time series that exhibit similar sub-sequences at arbitrary positions. Additionally, we demonstrate the practicality of the proposed measure on a sample data set of synthetic time series with artificially implanted patterns, and discuss the implications for real-life data mining applications.

Paper Citation

in Harvard Style

Spiegel S. and Albayrak S. (2012). An Order-invariant Time Series Distance Measure - Position on Recent Developments in Time Series Analysis . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 264-268. DOI: 10.5220/0004165602640268

in Bibtex Style

@conference{kdir12,
author={Stephan Spiegel and Sahin Albayrak},
title={An Order-invariant Time Series Distance Measure - Position on Recent Developments in Time Series Analysis},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={264-268},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004165602640268},
isbn={978-989-8565-29-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - An Order-invariant Time Series Distance Measure - Position on Recent Developments in Time Series Analysis
SN - 978-989-8565-29-7
AU - Spiegel S.
AU - Albayrak S.
PY - 2012
SP - 264
EP - 268
DO - 10.5220/0004165602640268