Package: TSLSTM 0.1.0

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>.

Authors:Dr. Ranjit Kumar Paul [aut, cre], Dr. Md Yeasin [aut]

TSLSTM_0.1.0.tar.gz
TSLSTM_0.1.0.zip(r-4.7)TSLSTM_0.1.0.zip(r-4.6)TSLSTM_0.1.0.zip(r-4.5)
TSLSTM_0.1.0.tgz(r-4.6-any)TSLSTM_0.1.0.tgz(r-4.5-any)
TSLSTM_0.1.0.tar.gz(r-4.7-any)TSLSTM_0.1.0.tar.gz(r-4.6-any)
TSLSTM_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
TSLSTM/json (API)

# Install 'TSLSTM' in R:
install.packages('TSLSTM', repos = c('https://ranjitstat.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.48 score 1 packages 4 scripts 300 downloads 1 exports 70 dependencies

Last updated from:276da206a1. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK205
source / vignettesOK241
linux-release-x86_64OK248
macos-release-arm64OK100
macos-oldrel-arm64OK89
windows-develOK111
windows-releaseOK110
windows-oldrelOK106
wasm-releaseOK150

Exports:ts.lstm

Dependencies:askpassbackportsbase64encclicolorspaceconfigcpp11curlfarverforecastfracdiffgenericsggplot2gluegreyboxgtableherehttrisobandjsonlitekeraslabelinglatticelifecyclelmtestmagrittrMAPAMASSMatrixmimenlmenloptrnnetopensslplotrixpngpracmaprocessxpsR6rappdirsRColorBrewerRcppRcppArmadilloRcppTOMLreticulaterlangrprojrootrstudioapiS7scalessmoothstatmodsystensorflowtexregtfautographtfrunstidyselecttimeDatetsutilsurcavctrsviridisLitewhiskerwithrxtableyamlzeallotzoo