ARIMA-Informed LSTM for Time Series Forecasting R package ARInfoLSTM version 0.1.0 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>. Software Imports: torch (>= 0.11.0), forecast (>= 8.21), ggplot2 (>= 3.4.0), cli (>= 3.6.0), coro, stats, utils Suggests: testthat (>= 3.0.0) Ranjit Kumar Paul <ranjitstat@gmail.com> Comprehensive R Archive Network (CRAN) Md Yeasin [aut], Ranjit Kumar Paul [aut, cre], Pushkar Bora [aut] GPL-3 2026-09-16 application/tgz https://CRAN.R-project.org/package=ARInfoLSTM doi:10.32614/CRAN.package.ARInfoLSTM