Forecasting Stock Market Indices Using Integration of Encoder, Decoder, and Attention Mechanism

Accurate forecasting of stock market indices is crucial for investors, financial analysts, and policymakers. The integration of encoder and decoder architectures, coupled with an attention mechanism, has emerged as a powerful approach to enhance prediction accuracy. This paper presents a novel frame...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Vol. 27; no. 1; p. 82
Main Author: Thach, Tien Thanh
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.01.2025
MDPI
Subjects:
Accuracy
attention mechanism
Bayesian optimization
Coders
Comparative analysis
Computational linguistics
Deep learning
encoder–decoder architecture
Forecasting
gated recurrent units
Language processing
long short-term memory
Machine translation
Natural language interfaces
Neural networks
Recurrent neural networks
Securities markets
Stock exchanges
Stock markets
Stock price indexes
Stock prices
Stocks
Time series
Voice recognition
Vietnam
gated recurrent units
encoder–decoder architecture
recurrent neural networks
Bayesian optimization
attention mechanism
long short-term memory
ISSN:1099-4300, 1099-4300
Online Access:Get full text
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Summary:Accurate forecasting of stock market indices is crucial for investors, financial analysts, and policymakers. The integration of encoder and decoder architectures, coupled with an attention mechanism, has emerged as a powerful approach to enhance prediction accuracy. This paper presents a novel framework that leverages these components to capture complex temporal dependencies and patterns within stock price data. The encoder effectively transforms an input sequence into a dense representation, which the decoder then uses to reconstruct future values. The attention mechanism provides an additional layer of sophistication, allowing the model to selectively focus on relevant parts of the input sequence for making predictions. Furthermore, Bayesian optimization is employed to fine-tune hyperparameters, further improving forecast precision. Our results demonstrate a significant improvement in forecast precision over traditional recurrent neural networks. This indicates the potential of our integrated approach to effectively handle the complex patterns and dependencies in stock price data.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e27010082