Elastic Partial Matching of Time Series

We consider a problem of elastic matching of time series. We propose an algorithm that automatically determines a subsequence b of a target time series b that best matches a query series a. In the proposed algorithm we map the problem of the best matching subsequence to the problem of a cheapest path in a DAG (directed acyclic graph). Our experimental results demonstrate that the proposed algorithm outperforms the commonly used Dynamic Time Warping in retrieval accuracy. 1 Motivation For many datasets we can easily and accurately extract the beginning and ending of patterns of interest. However in some domains it is non-trivial to define the exact beginning and ending of a pattern within a longer sequence. This is a problem because if the endpoints are incorrectly specified they can swamp the distance calculation in otherwise similar objects. For concreteness we will consider an example of just such a domain and show that Minimal Variance Matching (MVM), proposed in this paper, can b...

• Type: Conference
• Year: 2005
• Where: PKDD
• Authors: Longin Jan Latecki, Vasilis Megalooikonomou, Qiang Wang, Rolf Lakämper, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh
• Keywords: Dynamic Time Warping, PKDD 2005, Target Time Series, Time Series