[R-pkgs] R Package dtwclust: Shape-based clustering of univariate time series
alexis.sarda at gmail.com
Sun Aug 23 20:14:06 CEST 2015
Time Series Clustering With Dynamic Time Warping Distance (DTW)
The dtwclust package attempts to consolidate some of the recent techniques
related to time series clustering under DTW and implement them in R. Most
of these algorithms make use of traditional clustering techniques
(partitional and hierarchical clustering) but change the distance
definition. In this case, the distance between time series is measured with
DTW is, however, computationally expensive, so several optimization
techniques exist. They mostly deal with bounding the DTW distance. These
bounds are only defined for time series of equal lengths. Nevertheless, if
the length of the time series of interest vary only slightly,
reinterpolating them to a common length is probably an appropriate solution.
Additionally, a recently proposed algorithm called k-Shape could serve as
an alternative. k-Shape clustering relies on custom distance and centroid
definitions, which are unrelated to DTW. The shape extraction algorithm
proposed therein is particularly interesting if time series can be
Many of the algorithms and optimizations require that all series have the
same length. The ones that don't are usually slow but can still be used.
Please see the references for more information.
- Partitional procedures are implemented by leveraging the `flexclust`
- Hierarchical procedures use the native `hclust` function.
- Cross-distances make use of the `proxy` package.
- The core DTW calculations are done by the `dtw` package.
- Plotting is done with the `ggplot2` package.
- Keogh's and Lemire's lower bounds
- DTW Barycenter Averaging
- k-Shape clustering
- TADPole clustering
Alexis Sardá Espinosa.
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