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.

@inproceedings{Spiegel-Invariance,
author = {Spiegel, Stephan and Albayrak, Sahin},
title = {An Order-Invariant Time Series Distance Measure},
booktitle = {KDIR 2012: Proceeding of 4th International Conference on Knowledge Discovery and Information Retrieval},
year = {2012},
location = {Barcelona, Spain},
publisher = {SciTePress Digital Library},
pages = {264-268}
}