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)