The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
The real-life time series data are hardly pure linear or nonlinear. Merging a linear time series model like the autoregressive moving average (ARMA) model with a nonlinear neural network model such as the Long Short-Term Memory (LSTM) model can be used as a hybrid model for more accurate modeling purposes. Both the autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models can be implemented. Details can be found in Box et al. (2015, ISBN: 978-1-118-67502-1) and Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>.
| Version: | 0.1.0 |
| Imports: | rugarch, tseries, tensorflow, keras, reticulate |
| Published: | 2024-02-28 |
| DOI: | 10.32614/CRAN.package.ARMALSTM |
| Author: | Debopam Rakshit [aut, cre], Ritwika Das [aut], Dwaipayan Bardhan [aut] |
| Maintainer: | Debopam Rakshit <rakshitdebopam at yahoo.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | ARMALSTM results |
| Reference manual: | ARMALSTM.pdf |
| Package source: | ARMALSTM_0.1.0.tar.gz |
| Windows binaries: | r-devel: ARMALSTM_0.1.0.zip, r-release: ARMALSTM_0.1.0.zip, r-oldrel: ARMALSTM_0.1.0.zip |
| macOS binaries: | r-release (arm64): ARMALSTM_0.1.0.tgz, r-oldrel (arm64): ARMALSTM_0.1.0.tgz, r-release (x86_64): ARMALSTM_0.1.0.tgz, r-oldrel (x86_64): ARMALSTM_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=ARMALSTM to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.