The DTW function performs dynamic time warping (DTW), which measures the similarity (warp distance) between two time series that vary in time or speed. You can use DTW to analyze any data that can be represented linearly—for example, video, audio, and graphics.
For example:
- In two videos, DTW can detect similarities in walking patterns, even if in one video the person is walking slowly and in another, the same person is walking fast.
- In audio, DTW can detect similarities in different speech speeds (and is therefore very useful in speech recognition applications).
Given an input table, a template table, and a mapping table, DTW compares each time series in the input table to the corresponding time series in the template table. The correspondence is defined by the mapping table.
For more information, see FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space. Stan Salvador & Philip Chan. KDD Workshop on Mining Temporal and Sequential Data, pp. 70-80, 2004 (http://cs.fit.edu/~pkc/papers/tdm04.pdf)