An Order-Invariant Time Series Distance Measure

AutorStephan Spiegel, Sahin Albayrak
QuelleKDIR: Proceedings of International Conference on Knowledge Discovery and Information Retrieval, Barcelona, Spain 
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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.