ARInfoLSTM: ARIMA-Informed LSTM for Time Series Forecasting

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>.

Version:	0.1.0
Imports:	torch (≥ 0.11.0), forecast (≥ 8.21), ggplot2 (≥ 3.4.0), cli (≥ 3.6.0), coro, stats, utils
Suggests:	testthat (≥ 3.0.0)
Published:	2026-09-16
DOI:	10.32614/CRAN.package.ARInfoLSTM (may not be active yet)
Author:	Md Yeasin [aut], Ranjit Kumar Paul [aut, cre], Pushkar Bora [aut]
Maintainer:	Ranjit Kumar Paul <ranjitstat at gmail.com>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	ARInfoLSTM results

Documentation:

Reference manual:

ARInfoLSTM.html , ARInfoLSTM.pdf

Downloads:

Package source:	ARInfoLSTM_0.1.0.tar.gz
Windows binaries:	r-devel: ARInfoLSTM_0.1.0.zip, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): ARInfoLSTM_0.1.0.tgz, r-release (x86_64): ARInfoLSTM_0.1.0.tgz, r-oldrel (x86_64): ARInfoLSTM_0.1.0.tgz

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