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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 ...
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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.
GTW considers both the alignment accuracy of each sequence pair and the similarity among pairs. On contrary, alignment with dynamic time warping (DTW) considers the pairs independently and minimizes only the distance between the two sequences in a given pair. Therefore, GTW generalizes DTW and could achieve a better alignment performance when ...
Dynamic time warping: measure similarity between two sequences which may vary in time or speed; Hirschberg's algorithm: finds the least cost sequence alignment between two sequences, as measured by their Levenshtein distance; Needleman–Wunsch algorithm: find global alignment between two sequences
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Approximating Dynamic Time Warping Distance Between Run-Length Encoded Strings Tim Zeitz and Nils Werner: Combining Predicted and Live Traffic with Time-Dependent A* Potentials 2021 Zhiyang He, Jason Li and Magnus Wahlström: Near-linear-time, Optimal Vertex Cut Sparsifiers in Directed Acyclic Graphs
Download as PDF; Printable version; In other projects Wikidata item; Appearance. ... Dynamic time warping; Graphical time warping This page was last edited on 15 ...