# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "ARInfoLSTM" in publications use:' type: software license: GPL-3.0-only title: 'ARInfoLSTM: ARIMA-Informed LSTM for Time Series Forecasting' version: 0.1.0 doi: 10.32614/CRAN.package.ARInfoLSTM abstract: 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) . authors: - family-names: Yeasin given-names: Md - family-names: Paul given-names: Ranjit Kumar email: ranjitstat@gmail.com - family-names: Bora given-names: Pushkar repository: https://ranjitstat.r-universe.dev commit: c0b31063f285d42fa89a1976f519ad0a28973f52 date-released: '2026-09-16' contact: - family-names: Paul given-names: Ranjit Kumar email: ranjitstat@gmail.com