A Hybrid GAS-ATT-LSTM Architecture for Predicting Non-Stationary Financial Time Series

A Hybrid GAS-ATT-LSTM Architecture for Predicting Non-Stationary Financial Time Series

This study proposes a hybrid approach to analyze and forecast non-stationary financial time series by combining statistical models with deep neural networks. A model is introduced that integrates three key components: the Generalized Autoregressive Score (GAS) model, which captures volatility dynami...

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Bibliographic Details
Main Authors:	Kevin Astudillo, Miguel Flores, Mateo Soliz, Guillermo Ferreira, José Varela-Aldás
Format:	Article
Language:	English
Published:	MDPI AG 2025-07-01
Series:	Mathematics
Subjects:	GAS LSTM attention mechanism hybrid model forecasting neural network
Online Access:	https://www.mdpi.com/2227-7390/13/14/2300
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