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In time series analysis, dynamic time warping (DTW) is an algorithm for measuring similarity between two temporal sequences, which may vary in speed. 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 ...
As each GTW subgraph is dual to a DTW graph, the maximum flow within each GTW subgraph can be solved in linear time through dynamic programming. In many applications, a rough approximate solution of the warping paths can be estimated, which could serve as the initialization to accelerate the solving process.
Earlier approaches include dynamic time warping (DTW) used for applications such as speech recognition. [54] Another traditional method for time warping is landmark registration, [55] [56] which aligns special features such as peak locations to an average location.
In comparison to other distance measures, (e.g. DTW (dynamic time warping) or LCS (longest common subsequence problem)), TWED is a metric. Its computational time complexity is O ( n 2 ) {\displaystyle O(n^{2})} , but can be drastically reduced in some specific situations by using a corridor to reduce the search space.
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In the second step, the derived feature sequences have to be brought into (temporal) correspondence. To this end, techniques related to dynamic time warping (DTW) or hidden Markov models (HMMs) are used to compute an optimal alignment between two given feature sequences.
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