Package: ARInfoLSTM
Type: Package
Title: ARIMA-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"
    ))
Description: Implements an ARIMA-Informed Long Short-Term Memory (LSTM)
        framework for univariate time series forecasting. The package
        integrates statistical information extracted from
        AutoRegressive Integrated Moving Average (ARIMA) models with
        deep learning-based LSTM architectures to improve forecasting
        accuracy, stability, and interpretability. Inspired by the
        philosophy of Physics-Informed Machine Learning (PIML), the
        proposed framework incorporates information from classical
        statistical models into neural network learning, creating a
        hybrid forecasting approach that combines domain knowledge with
        data-driven intelligence. The methodology is motivated by
        hybrid forecasting framework proposed by Yeasin and Paul (2024)
        <doi:10.1007/s11227-023-05542-3>.
License: GPL-3
Encoding: UTF-8
Imports: torch (>= 0.11.0), forecast (>= 8.21), ggplot2 (>= 3.4.0), cli
        (>= 3.6.0), coro, stats, utils
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
Config/roxygen2/version: 8.1.0
NeedsCompilation: no
Packaged: 2026-09-17 15:43:51 UTC; root
Author: Md Yeasin [aut], Ranjit Kumar Paul [aut, cre], Pushkar Bora
        [aut]
Maintainer: Ranjit Kumar Paul <ranjitstat@gmail.com>
Repository: https://ranjitstat.r-universe.dev
Date/Publication: 2026-09-16 11:40:02 UTC
RemoteUrl: https://github.com/cran/ARInfoLSTM
RemoteRef: HEAD
RemoteSha: c0b31063f285d42fa89a1976f519ad0a28973f52