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license: MIT
title: 'GARCHInfoLSTM: GARCH-Informed LSTM Model for Volatility Forecasting'
version: 0.1.0
doi: 10.32614/CRAN.package.GARCHInfoLSTM
abstract: The proposed Generalized Autoregressive Conditional Heteroskedasticity (GARCH)-informed
  Long Short-Term Memory (LSTM) model follows the concept of physics-informed machine
  learning (PIML) by integrating established econometric knowledge of price volatility
  into a data-driven forecasting framework. In the model, conditional volatility estimated
  from the GARCH process is incorporated as an additional explanatory signal or volatility-based
  weighting component within the LSTM architecture. This enables the LSTM to learn
  nonlinear temporal dependencies while remaining informed by the underlying characteristics
  of agricultural price series, including volatility clustering, heteroscedasticity
  and market uncertainty. The optimized weighting parameter, lambda, controls the
  contribution of the GARCH-derived volatility information to the final prediction.
  Thus, the model combines the statistical interpretability of GARCH with the nonlinear
  learning capability of LSTM, producing a hybrid PIML framework that is more responsive
  to both normal price movements and periods of extreme market volatility. The methodology
  is motivated by hybrid forecasting framework proposed by Yeasin and Paul (2024)
  <https://doi.org/10.1007/s11227-023-05542-3>.
authors:
- family-names: Yeasin
  given-names: Md
- family-names: Paul
  given-names: Ranjit Kumar
  email: ranjitstat@gmail.com
- family-names: Mandal
  given-names: Manojit
- family-names: Bora
  given-names: Pushkar
repository: https://ranjitstat.r-universe.dev
commit: 777cd3c04ed81f78b4b2242474c409dc9b83406c
date-released: '2026-09-21'
contact:
- family-names: Paul
  given-names: Ranjit Kumar
  email: ranjitstat@gmail.com