Package: RWInfoLSTM
Type: Package
Title: Random Walk-Informed LSTM for Time Series Forecasting
Version: 0.1.0
Authors@R: c(
    person(given = "Md", family = "Yeasin", role = "aut"),
    person(given = "Ranjit Kumar", family = "Paul",
           email = "ranjitstat@gmail.com", role = c("aut", "cre")),
    person(given = "Pushkar", family = "Bora", role = "aut")
  )
Maintainer: Ranjit Kumar Paul <ranjitstat@gmail.com>
Description: Implements a Random Walk (RW)-informed Long Short-Term
        Memory (LSTM) network for univariate time series forecasting.
        The LSTM is trained on the full series with a composite loss
        that blends the usual mean squared error (MSE) between
        predictions and observations with the MSE between predictions
        and the fitted Random Walk, weighted by a parameter lambda (0
        gives a standard LSTM, 1 gives a fully RW-guided LSTM). The
        Random Walk, either simple or with drift, is selected
        automatically by its in-sample root mean squared error (RMSE),
        and the lambda value with the lowest training RMSE is retained.
        The LSTM hyperparameters can be supplied manually or tuned by
        grid search, and functions for out-of-sample forecasting,
        accuracy measures and plots are provided. The methodology is
        motivated by hybrid forecasting framework proposed by Yeasin
        and Paul (2024) <doi:10.1007/s11227-023-05542-3>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: torch (>= 0.11.0), coro, ggplot2 (>= 3.4.0), cli (>= 3.6.0),
        stats, utils
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
Config/roxygen2/version: 8.1.1
NeedsCompilation: no
Packaged: 2026-10-29 17:12:48 UTC; root
Author: Md Yeasin [aut], Ranjit Kumar Paul [aut, cre], Pushkar Bora
        [aut]
Repository: https://cran.r-universe.dev
Date/Publication: 2026-10-29 16:16:30 UTC
RemoteUrl: https://github.com/cran/RWInfoLSTM
RemoteRef: HEAD
RemoteSha: afc8339f300e3a5d975d9f580a38bf8815a35f83