Package: tsLSTMx
Type: Package
Title: Predict Time Series Using LSTM Model Including Exogenous
        Variable to Denote Zero Values
Version: 0.1.0
Authors@R: c(person("Sandip", "Garai", role = c("aut","cre"), email = "sandipnicksandy@gmail.com"),
    person("Krishna Pada", "Sarkar", role = "aut", email = "kpsarkarrbi@gmail.com"))
Author: Sandip Garai [aut, cre], Krishna Pada Sarkar [aut]
Maintainer: Sandip Garai <sandipnicksandy@gmail.com>
Description: It is a versatile tool for predicting time series data
        using Long Short-Term Memory (LSTM) models. It is specifically
        designed to handle time series with an exogenous variable,
        allowing users to denote whether data was available for a
        particular period or not. The package encompasses various
        functionalities, including hyperparameter tuning, custom loss
        function support, model evaluation, and one-step-ahead
        forecasting. With an emphasis on ease of use and flexibility,
        it empowers users to explore, evaluate, and deploy LSTM models
        for accurate time series predictions and forecasting in diverse
        applications. More details can be found in Garai and Paul
        (2023) <doi:10.1016/j.iswa.2023.200202>.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: tensorflow, AllMetrics, keras, reticulate
NeedsCompilation: no
Packaged: 2026-09-23 08:12:04 UTC; root
Config/pak/sysreqs: libpng-dev python3
Repository: https://sandipgarai.r-universe.dev
Date/Publication: 2024-01-13 02:40:12 UTC
RemoteUrl: https://github.com/cran/tsLSTMx
RemoteRef: HEAD
RemoteSha: 9603efaaee23dad3520f9532509f936f6353e40b