DTW: dynamic time warping 动态时间规整

Basically, DTW (dynamic time warping) is an algorithm to output cumulative distance of two time sequences, which is widely used e.g. for classification and clustering.

For example, when using k-mean for clustering, we can use DTW as distance function. Here is one of such nice instances (using R: http://www.rdatamining.com/examples/ts-mining)

Relevant information from Anshul's email.  

A review of DTW http://csdl.ics.hawaii.edu/techreports/08-04/08-04.pdf

Code:

Python code: https://mlpy.fbk.eu/

R code: http://dtw.r-forge.r-project.org/

JAVA code: http://java-ml.sourceforge.net/ or http://code.google.com/p/fastdtw/

MATLAB: the samplealign() function in the bioinformatics toolbox does DTW

There is also the global alignment kernel that is faster and more accurate that DTW. It can be used to compute distance between time series to be used in clustering allowing for all possible global alignments. Here is code http://www.iip.ist.i.kyoto-u.ac.jp/member/cuturi/GA.html

Here is an example (in Chinese) from Ckary's blog: