Packaged: 2017-08-23 19:17:56 UTC; LeodolterM

Packaged: 2017-10-10 08:40:56 UTC; LeodolterM

Packaged: 2018-01-13 14:28:23 UTC; LeodolterM
Suggests: knitr, dtw, rbenchmark, rmarkdown, gridExtra

Packaged: 2018-05-11 15:48:30 UTC; LeodolterM

Packaged: 2018-05-13 19:51:14 UTC; LeodolterM

Description: The Dynamic Time Warping (DTW) distance for time series allows non-linear alignments of time series to match  similar patterns in time series of different lengths and or different speeds. Beside the traditional implementation of the DTW algorithm, the specialities of this package are, (1) the incremental calculation, which is specifically useful for life data streams due to computationally efficiency, (2) the vector based implementation of the traditional DTW algorithm which is faster because no matrices are allocated and is especially useful for computing distance matrices of pairwise DTW distances for many time series  and (3) the combination of incremental and vector-based calculation. C++ in the heart. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) <DOI:10.1109/TASSP.1978.1163055>.
Imports: Rcpp (>= 0.12.8), RcppParallel, ggplot2, scales, parallel,
Packaged: 2018-09-11 06:30:13 UTC; LeodolterM
Suggests: knitr, dtw, rmarkdown, gridExtra, testthat, dtwclust,

Packaged: 2019-01-02 11:39:31 UTC; LeodolterM

Description: The Dynamic Time Warping (DTW) distance measure for time series allows non-linear alignments of time series to match  similar patterns in time series of different lengths and or different speeds. IncDTW is characterized by (1) the incremental calculation of DTW (reduces runtime complexity to a linear level for updating the DTW distance) - especially for life data streams or subsequence matching, (2) the vector based implementation of DTW which is faster because no matrices are allocated (reduces the space complexity from a quadratic to a linear level in the number of observations) - for all runtime intensive DTW computations, (3) the subsequence matching algorithm runDTW, that efficiently finds the k-NN to a query pattern in a long time series, and (4) C++ in the heart. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) <DOI:10.1109/TASSP.1978.1163055>.
Packaged: 2019-04-08 14:35:05 UTC; LeodolterM
parallelDist, microbenchmark, rucrdtw, proxy, R.rsp