Dynamic Time Warping (DTW) [1] is one of wellknown distance measures between a pairwise of time series. The main idea of DTW is to compute the distance from the matching of similar elements between time series. It uses the dynamic programming technique to find the optimal temporal matching between elements of two time series.
For instance, similarities in walking could be detected using DTW, even if one person was walking faster than the other, or if there were accelerations and decelerations during the course of an observation. DTW has been applied to temporal sequences of video, audio, and graphics data — indeed, any data that can be turned into a linear sequence can be analyzed with DTW. A well known application has been automatic speech recognition, to cope with different speaking speeds. Other applications include speaker recognition and online signature recognition. It can also be used in partial shape matching application.
In general, DTW is a method that calculates an optimal match between two given sequences (e.g. time series) with certain restriction and rules:
[1] Sakoe, Hiroaki, and Seibi Chiba. "Dynamic programming algorithm optimization for spoken word recognition." IEEE transactions on acoustics, speech, and signal processing 26, no. 1 (1978): 4349.
Paper  Code  Results  Date  Stars 

Task  Papers  Share 

Dynamic Time Warping  89  30.80% 
Time Series  65  22.49% 
General Classification  17  5.88% 
Time Series Classification  16  5.54% 
Time Series Analysis  9  3.11% 
Time Series Clustering  4  1.38% 
Decision Making  4  1.38% 
Temporal Sequences  4  1.38% 
Time Series Averaging  4  1.38% 
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