IncDTW
1.1.4.6Incremental Calculation of Dynamic Time Warping
Overview
The Dynamic Time Warping (DTW) distance measure for time series allows non-linear alignments of time series to match similar patterns in time series of different lengths and or different speeds. IncDTW is characterized by (1) the incremental calculation of DTW (reduces runtime complexity to a linear level for updating the DTW distance) - especially for life data streams or subsequence matching, (2) the vector based implementation of DTW which is faster because no matrices are allocated (reduces the space complexity from a quadratic to a linear level in the number of observations) - for all runtime intensive DTW computations, (3) the subsequence matching algorithm runDTW, that efficiently finds the k-NN to a query pattern in a long time series, and (4) C++ in the heart. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) DOI:10.1109/TASSP.1978.1163055. For details about this package, Dynamic Time Warping and Incremental Dynamic Time Warping please see "IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping" by Leodolter et al. (2021) doi:10.18637/jss.v099.i09.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-07-3013 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- WARNING2026-07-2712 OK · 0 NOTE · 1 WARNING · 0 ERROR · 0 FAILURE
- OK2026-04-2512 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Show 3 earlier snapshots
- NOTE2026-04-2211 OK · 3 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1810 OK · 3 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-1011 OK · 3 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 61%
- Documented parameters
- 100%
- Return-value docs
- 78%
- References docs
- 53%
Downloads
Dependencies
Code & Tests
People & History
20 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.1.4.6Latest
- 1.1.4.52025-10-07 · diff ↗
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- 1.1.4.42022-03-16 · diff ↗
- 1.1.4.32021-09-23 · diff ↗
- RR 4.1.0 released · 2021-05-18
- 1.1.4.22020-10-09 · diff ↗
- RR 4.0.0 released · 2020-04-24
- 1.1.3.12020-04-01 · diff ↗
- 1.1.32019-12-18 · diff ↗
- 1.1.22019-06-27 · diff ↗
- 1.1.12019-05-24 · diff ↗
Show 14 earlier events
- RR 3.6.0 released · 2019-04-26
- 1.1.02019-04-08 · diff ↗
- 1.0.52019-01-03 · diff ↗
- 1.0.42018-09-11 · diff ↗
- 1.0.32018-05-14 · diff ↗
- 1.0.22018-05-11 · diff ↗
- RR 3.5.0 released · 2018-04-23
- 1.0.12018-01-13 · diff ↗
- 1.0.02017-10-10 · diff ↗
- 0.1.32017-08-23 · diff ↗
- 0.1.22017-08-23 · diff ↗
- 0.1.12017-08-04 · diff ↗
- 0.1.02017-07-03
- RR 3.4.0 released · 2017-04-21
Package metadata
- First published
- 2017-07-03
- Total releases
- 20 / 9 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 506 KB / 3 files
- Download size
- 1.5 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
Cite
Cite this package
Run in R for the authors' preferred citation:
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