Indexing multi-dimensional time-series with support for multiple distance measures

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Towards parameter-free data mining (2004, methodology): “For example, CDM is a dissimilarity measure; if it could be modified to be a distance measure, or better still, a distance metric, we could avail of a wealth of pruning and indexing techniques to speed up classification [30], clustering [6], and similarity search [34].”
Modeling multiple time series for anomaly detection (2005, methodology): “We use the Greedy-Split algorithm [8] to generate a model for one time series.”
Automated extraction and parameterization of motions in large data sets (2004, methodology): “Example approximations include the (cid:2)rst few coef(cid:2)cients of a Fourier [Agrawal et al. 1993] or wavelet [Chan and Fu 1999] transform, the average values in adjacent windows [Keogh et al. 2001], and bounding boxes [Vlachos et al. 2003].”
Robust and fast similarity search for moving object trajectories (2005, background): “4 on NHL data [5], a mixed data set [34] and a randomwalk trajectory data set [6, 19].”
A review on time series data mining (2011, methodology): “Minimum bounding rectangle (MBR) is also a common technique for time series indexing (Chu and Wong, 1999; Vlachos et al., 2003).”
Path Similarity Skeleton Graph Matching (2008, methodology): “When LCSS is applied to sequences of numeric values, one needs to set a threshold that determines when values of corresponding points are treated as equal [ 45 ].”