R: Dynamic Time Warping distance.

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 `dtw` for more information.

### 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)

[Package TSdist version 3.6 Index]