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
|
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
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