DTWDistance {TSdist} | R Documentation |
Dynamic Time Warping distance.
Description
Computes the Dynamic Time Warping distance between a pair of numeric time series.
Usage
DTWDistance(x, y, ...)
Arguments
x |
Numeric vector containing the first time series. |
y |
Numeric vector containing the second time series. |
... |
Additional parameters for the function. See |
Details
This is simply a wrapper for the dtw
function of package dtw. As such, all the functionalities of the dtw
function are also available when using this function.
Value
d |
The computed distance between the pair of series. |
Author(s)
Usue Mori, Alexander Mendiburu, Jose A. Lozano.
References
Giorgino T (2009). Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package. Journal of Statistical Software, 31(7), pp. 1-24. URL:http://www.jstatsoft.org/v31/i07/
Cuturi, M. (2011). Fast Global Alignment Kernels. In Proceedings of the 28th International Conference on Machine Learning (pp. 929–936).
Gaidon, A., Harchaoui, Z., & Schmid, C. (2011). A time series kernel for action recognition. In BMVC 2011 - British Machine Vision Conference (pp. 63.1–63.11).
Marteau, P.-F., & Gibet, S. (2014). On Recursive Edit Distance Kernels With Applications To Time Series Classification. IEEE Transactions on Neural Networks and Learning Systems, PP(6), 1–13.
Lei, H., & Sun, B. (2007). A Study on the Dynamic Time Warping in Kernel Machines. In 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (pp. 839–845).
Pree, H., Herwig, B., Gruber, T., Sick, B., David, K., & Lukowicz, P. (2014). On general purpose time series similarity measures and their use as kernel functions in support vector machines. Information Sciences, 281, 478–495.
See Also
To calculate a lower bound of the DTW distance see LBKeoghDistance
.
To calculate this distance measure using ts
, zoo
or xts
objects see TSDistances
. To calculate distance matrices of time series databases using this measure see TSDatabaseDistances
.
Examples
# The objects example.series3 and example.series4 are two
# numeric series of length 100 and 120 contained in the TSdist
# package
data(example.series3)
data(example.series4)
# For information on their generation and shape see
# help page of example.series.
help(example.series)
# Calculate the basic DTW distance for two series of different length.
DTWDistance(example.series3, example.series4)
# Calculate the DTW distance for two series of different length
# with a sakoechiba window of size 30:
DTWDistance(example.series3, example.series4, window.type="sakoechiba", window.size=30)
# Calculate the DTW distance for two series of different length
# with an assymetric step pattern
DTWDistance(example.series3, example.series4, step.pattern=asymmetric)