Package: DTWBI 1.1
Emilie Poisson-Caillault
DTWBI: Imputation of Time Series Based on Dynamic Time Warping
Functions to impute large gaps within time series based on Dynamic Time Warping methods. It contains all required functions to create large missing consecutive values within time series and to fill them, according to the paper Phan et al. (2017), <doi:10.1016/j.patrec.2017.08.019>. Performance criteria are added to compare similarity between two signals (query and reference).
Authors:
DTWBI_1.1.tar.gz
DTWBI_1.1.tar.gz(r-4.5-noble)DTWBI_1.1.tar.gz(r-4.4-noble)
DTWBI_1.1.tgz(r-4.4-emscripten)DTWBI_1.1.tgz(r-4.3-emscripten)
DTWBI.pdf |DTWBI.html✨
DTWBI/json (API)
| # Install 'DTWBI' in R: |
| install.packages('DTWBI', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- dataDTWBI - Six univariate signals as example for DTWBI package
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 years agofrom:022fabcc17. Checks:1 OK, 1 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Feb 05 2025 |
| R-4.5-linux | NOTE | Feb 05 2025 |
Exports:compute.fa2compute.fbcompute.fsdcompute.nmaecompute.rmsecompute.simdist_afbdtwDTWBI_univariategapCreationlocal.derivative.ddtwminCost
Dependencies:classdata.tabledtwe1071entropyjsonlitelsaMASSproxyrlistSnowballCXMLyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Imputation of Time Series Based on Dynamic Time Warping | DTWBI-package DTWBI |
| FA2 | compute.fa2 |
| Fractional Bias (FB) | compute.fb |
| Fraction of Standard Deviation (FSD) | compute.fsd |
| Normalized Mean Absolute Error (NMAE) | compute.nmae |
| Root Mean Square Error (RMSE) | compute.rmse |
| Similarity | compute.sim |
| Six univariate signals as example for DTWBI package | dataDTWBI |
| Adaptive Feature Based Dynamic Time Warping algorithm | dist_afbdtw |
| DTWBI algorithm for univariate signals | DTWBI_univariate |
| Gap creation | gapCreation |
| Local derivative estimate to compute DDTW | local.derivative.ddtw |
| DTW-based methods for univariate signals | minCost |