TSLSTM: Long Short Term Memory (LSTM) Model for Time Series Forecasting

The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Min-Max transformation has been used for data preparation. Here, we have used one LSTM layer as a simple LSTM model and a Dense layer is used as the output layer. Then, compile the model using the loss function, optimizer and metrics. This package is based on Keras and TensorFlow modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.

Version:	0.1.0
Imports:	keras, tensorflow, tsutils, stats
Published:	2022-01-13
DOI:	10.32614/CRAN.package.TSLSTM
Author:	Dr. Ranjit Kumar Paul [aut, cre], Dr. Md Yeasin [aut]
Maintainer:	Dr. Ranjit Kumar Paul <ranjitstat at gmail.com>
License:	GPL-3
NeedsCompilation:	no
In views:	TimeSeries
CRAN checks:	TSLSTM results

Documentation:

Reference manual:

TSLSTM.html , TSLSTM.pdf

Downloads:

Package source:	TSLSTM_0.1.0.tar.gz
Windows binaries:	r-devel: TSLSTM_0.1.0.zip, r-release: TSLSTM_0.1.0.zip, r-oldrel: TSLSTM_0.1.0.zip
macOS binaries:	r-release (arm64): TSLSTM_0.1.0.tgz, r-oldrel (arm64): TSLSTM_0.1.0.tgz, r-release (x86_64): TSLSTM_0.1.0.tgz, r-oldrel (x86_64): TSLSTM_0.1.0.tgz

Reverse dependencies:

Reverse imports:

WaveletLSTM

Linking:

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