Comparative Evaluation of Standard, Bayesian and Quantile LSTM Networks for Time-Series Forecasting of Indian Stock Prices
Year: 2025
Type: article
Abstract: Long Short-Term Memory (LSTM) neural networks have recently gained prominence as one of the most accurateand reliable deep learning architectures for time-series forecasting. Despite their growing popularity, there remainslimited comparative research on different variants of LSTM models within financial forecasting contexts. This studyaims at bridging this gap through a comprehensive evaluation of three LSTM-based approaches—Standard LSTM,Bayesian LSTM, and Quantile Regression LSTM—applied to the prediction of NIFTY-10 stock prices in the Indianstock market. Through a detailed comparison of predictive accuracy, uncertainty quantification, and directionalperformance, this work identifies the most robust and consistent LSTM model for stock market forecasting.
Author: Pramukh G
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7103976028
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