Authors | Introduction | Function in the R package | Background | Reference
Package: EMDANNhybrid 0.2.0
Pankaj Das
EMDANNhybrid: Empirical Mode Decomposition Based Artificial Neural Network Model
Application of empirical mode decomposition based artificial neural network model for nonlinear and non stationary univariate time series forecasting. For method details see (i) Choudhury (2019) <https://www.indianjournals.com/ijor.aspx?target=ijor:ijee3&volume=55&issue=1&article=013>; (ii) Das (2020) <https://www.indianjournals.com/ijor.aspx?target=ijor:ijee3&volume=56&issue=2&article=002>.
Authors:
EMDANNhybrid_0.2.0.tar.gz
EMDANNhybrid_0.2.0.tar.gz(r-4.7-any)EMDANNhybrid_0.2.0.tar.gz(r-4.6-any)
EMDANNhybrid_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
EMDANNhybrid/json (API)
| # Install 'EMDANNhybrid' in R: |
| install.packages('EMDANNhybrid', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:c9c4e17412. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 165 | ||
| source / vignettes | OK | 203 | ||
| linux-release-x86_64 | OK | 156 | ||
| wasm-release | OK | 150 |
Exports:EMDANNhybrid
Dependencies:clicolorspacecpp11dotCall64EMDfarverfieldsforecastfracdiffgenericsggplot2gluegtableisobandlabelinglatticelifecyclelmtestlocfitmagrittrmapsnlmennetR6RColorBrewerRcppRcppArmadillorlangS7scalesspamtimeDateurcavctrsviridisLitewithrzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| New Ensemble Hybrid Machine Learning Model | EMDANNhybrid |