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Methods › Sequential › Time Series Analysis › DTW

Dynamic Time Warping

DTW

159 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Dynamic Time Warping (DTW) [1] is one of well-known 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. Every index from the first sequence must be matched with one or more indices from the other sequence, and vice versa
  2. The first index from the first sequence must be matched with the first index from the other sequence (but it does not have to be its only match)
  3. The last index from the first sequence must be matched with the last index from the other sequence (but it does not have to be its only match)
  4. The mapping of the indices from the first sequence to indices from the other sequence must be monotonically increasing, and vice versa, i.e. if j>i are indices from the first sequence, then there must not be two indices l>k in the other sequence, such that index i is matched with index l and index j is matched with index k, and vice versa.

[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): 43-49.

See Code · dynamictimewarping.github.io/

Papers archive 2025-07-28

30 shown of 159, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 142 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Dynamic Time Warping125
Time Series84
Time Series Analysis73
Clustering28
Time Series Classification21
General Classification17
Classification12
Anomaly Detection9
Action Recognition7
Metric Learning6
Representation Learning6
Retrieval6
Time Series Clustering6
Computational Efficiency5
Data Augmentation5
Temporal Sequences5
Decision Making4
Keyword Spotting4
Time Series Alignment4
Time Series Averaging4

Usage over time archive 2025-07-28

Papers per year tagged with DTW: 2008 to 2025, peak 23 23 0 2008: 1 paper 2008 2009: 0 papers 2010: 0 papers 2010 2011: 1 paper 2012: 0 papers 2012 2013: 1 paper 2014: 2 papers 2014 2015: 3 papers 2016: 11 papers 2016 2017: 9 papers 2018: 12 papers 2018 2019: 16 papers 2020: 20 papers 2020 2021: 23 papers 2022: 17 papers 2022 2023: 22 papers 2024: 12 papers 2024 2025: 9 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (159 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Time Series Analysis

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