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ARMALSTM  

Fitting of Hybrid ARMA-LSTM Models
View on CRAN: Click here


Download and install ARMALSTM package within the R console
Install from CRAN:
install.packages("ARMALSTM")

Install from Github:
library("remotes")
install_github("cran/ARMALSTM")

Install by package version:
library("remotes")
install_version("ARMALSTM", "0.1.0")



Attach the package and use:
library("ARMALSTM")
Maintained by
Debopam Rakshit
[Scholar Profile | Author Map]
First Published: 2024-02-28
Latest Update: 2024-02-28
Description:
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) .
How to cite:
Debopam Rakshit (2024). ARMALSTM: Fitting of Hybrid ARMA-LSTM Models. R package version 0.1.0, https://cran.r-project.org/web/packages/ARMALSTM. Accessed 21 Feb. 2025.
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