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>.

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