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

Stephan Spiegel, Sahin Albayrak

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.

References

  1. Batista, G. and Wang, X. (2011). A complexity-invariant distance measure for time series. SIAM Internation Conference on Data Mining (SDM)on Data Mining, Philadelphia, PA, USA.
  2. Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., and Keogh, E. (2008). Querying and mining of time series data: experimental comparison of representations and distance measures. Time, 1(2):15421552.
  3. Keogh, E. (2003). Efficiently finding arbitrarily scaled patterns in massive time series databases. volume 2838 of Lecture Notes in Computer Science, pages 253-265. Springer Berlin / Heidelberg.
  4. Keogh, E. and Ratanamahatana, C. A. (2005). Exact indexing of dynamic time warping. Knowl. Inf. Syst., 7(3):358-386.
  5. Lin, J., Keogh, E., and Lonardi, S. (2004). Visualizing and discovering non-trivial patterns in large time series databases. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, volume 4, page 61. SAGE Publications.
  6. Marwan, N. (2008). A historical review of recurrence plots. The European Physical Journal Special Topics, 164(1):3-12.
  7. Marwan, N., Carmenromano, M., Thiel, M., and Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438(5-6):237-329.
  8. Spiegel, S., Gaebler, J., Lommatzsch, A., De Luca, E., and Albayrak, S. (2011a). Pattern recognition and classification for multivariate time series. In KDD-2011: Proceeding of ACM International Workshop on Knowledge Discovery from Sensor Data (SensorKDD-2011), San Diego, CA, USA. ACM.
  9. Spiegel, S., Jain, B.-J., De Luca, E., and Albayrak, S. (2011b). Pattern recognition in multivariate time series - dissertation proposal. In CIKM 2011: Proceedings of 4th Workshop for Ph.D. Students in Information and Knowledge Management (PIKM 2011), Glasgow, UK. ACM.
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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