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: 2026-09-08 11:50:21 UTC; YEASIN
Author: Md Yeasin [aut],
  Ranjit Kumar Paul [aut, cre],
  Pushkar Bora [aut]
Maintainer: Ranjit Kumar Paul <ranjitstat@gmail.com>
Repository: CRAN
Date/Publication: 2026-09-16 11:40:02 UTC
