Package: TSLSTMplus
Title: Long-Short Term Memory for Time-Series Forecasting, Enhanced
Version: 1.0.6
Authors@R: c(
    person(given = "Jaime", family = "Pizarroso Gonzalo",
      email = "jpizarroso@comillas.edu", role = c("aut","ctb", "cre")),
	person(given = "Antonio", family = "Muñoz San Roque",
      email = "antonio.munoz@iit.comillas.edu", role = "aut")
    )
Author: Jaime Pizarroso Gonzalo [aut, ctb, cre], Antonio Muñoz San
        Roque [aut]
Maintainer: Jaime Pizarroso Gonzalo <jpizarroso@comillas.edu>
Description: 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>.
License: GPL-3
Encoding: UTF-8
Imports: keras, tensorflow, stats, abind
NeedsCompilation: no
RoxygenNote: 7.3.2
Date: 2025-02-03
Config/pak/sysreqs: libpng-dev python3
Repository: https://jaipizgon.r-universe.dev
Date/Publication: 2025-02-03 12:14:27 UTC
RemoteUrl: https://github.com/jaipizgon/tslstmplus
RemoteRef: HEAD
RemoteSha: e8d6ff7882d23c0a9f2331866d30bc9db7ba9634
Packaged: 2026-09-21 08:36:37 UTC; root