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: 2024-01-11 18:04:43 UTC; user
Repository: CRAN
Date/Publication: 2024-01-12 09:30:02 UTC
Built: R 4.6.0; ; 2026-04-26 15:39:30 UTC; unix
