FastTSDistances: Fast dissimilarity computations for time series

Description Usage Arguments Value References See Also

Version of compDist which operates on a list of time series and returns a distance matrix instead of a single distance. Saves computation time compared to naive n^2 calls of the original function by computing the SAX representations and single time series compression lengths only once for each time series (not in every distance computation).

1	compDistTSList(tsList, symbolCount = 8, symbolLimits = NULL)

`tsList`	1) A list of numeric vectors/matrixes (uni- or multi-variate time series). The dissimilarity of the list to itself (each time series to each time series) will be computed, resulting in a symmetric dissimilarity matrix. 2) A list with two components, each being a list of numeric vectors/ matrixes (uni- or multi-variate time series). The dissimilarity of each time series from the 1st component to each time series from the 2nd component will be computed.
`symbolCount`	Number of SAX symbols. Boundaries for the intervals will be determined based on the standard normal distribution. As an alternative, you can supply the boundaries directly.
`symbolLimits`	Interval boundaries which will be used to convert the time series to a SAX representation. Should be a monotonically increasing vector starting with -Inf and ending with +Inf. The parameter `symbolCount` is ignored if you supply a value here.

The dissimilarity matrix with each entry being a numeric from the range [0,1].

Keogh, E., Lonardi, S., Ratanamahatana, C. A., Wei, L., Lee, S.-H. & Handley, J. (2007). Compression-based data mining of sequential data. Data Mining and Knowledge Discovery, 14(1), 99–129.

Li, M., Badger, J. H., Chen, X., Kwong, S., Kearney, P. & Zhang, H. (2001). An information-based sequence distance and its application to whole mitochondrial genome phylogeny. Bioinformatics, 17(2), 149–154.

Other compression-based distances: compDist

Jakob-Bach/FastTSDistances documentation built on May 13, 2019, 1:15 p.m.