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ARInfoLSTM  

ARIMA-Informed LSTM for Time Series Forecasting
View on CRAN: Click here


Download and install ARInfoLSTM package within the R console
Install from CRAN:
install.packages("ARInfoLSTM")

Install from Github:
library("remotes")
install_github("cran/ARInfoLSTM")

Install by package version:
library("remotes")
install_version("ARInfoLSTM", "0.1.0")



Attach the package and use:
library("ARInfoLSTM")
Maintained by
Ranjit Kumar Paul
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-09-16
Latest Update: 2026-09-16
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>.
How to cite:
Ranjit Kumar Paul (2026). ARInfoLSTM: ARIMA-Informed LSTM for Time Series Forecasting. R package version 0.1.0, https://cran.r-project.org/web/packages/ARInfoLSTM. Accessed 24 Oct. 2026.
Previous versions and publish date:
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Complete documentation for ARInfoLSTM
Functions, R codes and Examples using the ARInfoLSTM R package
Full ARInfoLSTM package functions and examples
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