Package: TSLSTMplus 1.0.6

TSLSTMplus: Long-Short Term Memory for Time-Series Forecasting, Enhanced

The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Customizable configurations for the model are allowed, improving the capabilities and usability of this model compared to other packages. 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:Jaime Pizarroso Gonzalo [aut, ctb, cre], Antonio Muñoz San Roque [aut]

TSLSTMplus_1.0.6.tar.gz
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TSLSTMplus.pdf |TSLSTMplus.html✨
TSLSTMplus/json (API)

# Install 'TSLSTMplus' in R:

install.packages('TSLSTMplus', repos = c('https://jaipizgon.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jaipizgon/tslstmplus/issues

On CRAN:

3.30 score 1 scripts 364 downloads 5 exports 34 dependencies

Last updated 17 days agofrom:e8d6ff7882. Checks:8 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Feb 03 2025
R-4.5-win	OK	Feb 03 2025
R-4.5-mac	OK	Feb 03 2025
R-4.5-linux	OK	Feb 03 2025
R-4.4-win	OK	Feb 03 2025
R-4.4-mac	OK	Feb 03 2025
R-4.3-win	OK	Feb 03 2025
R-4.3-mac	OK	Feb 03 2025

Exports:lagmatrix LSTMModel minmax_scale ts.lstm ts.prepare.data

Dependencies:abind backports base64enc cli config generics glue here jsonlite keras lattice lifecycle magrittr Matrix png processx ps R6 rappdirs Rcpp RcppTOML reticulate rlang rprojroot rstudioapi tensorflow tfautograph tfruns tidyselect vctrs whisker withr yaml zeallot

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Create lead/lags of a variable	lagmatrix
LSTMModel class	LSTMModel
Min-Max Scaling of a Matrix	minmax_scale
Predict using a Trained LSTM Model	predict.LSTMModel
Summary of a Trained LSTM Model	summary.LSTMModel
Long Short Term Memory (LSTM) Model for Time Series Forecasting	ts.lstm
Prepare data for Long Short Term Memory (LSTM) Model for Time Series Forecasting	ts.prepare.data