ARMALSTM: Fitting of Hybrid ARMA-LSTM Models

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

Documentation:

Reference manual:

ARMALSTM.pdf

Downloads:

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

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