Leveraging the Strengths of Long Short-Term Memory and Gated Recurrent Unit Architectures for Indian Stock Market Prediction-Reference-Cited by-同舟云学术

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Leveraging the Strengths of Long Short-Term Memory and Gated Recurrent Unit Architectures for Indian Stock Market Prediction

Published:2024-02-09 Issue: Volume: Page:267-272
ISSN:
Container-title:2024 International Conference on Emerging Systems and Intelligent Computing (ESIC)
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Author:

Jena Prem Purusottam¹,Das Kaberi²,Mishra Debahuti¹,Lenka Arundhati¹,Mishra Sashikala³,Mallick Pradeep Kumar⁴

Affiliation:

1. Siksha ‘O’ Anusandhan (Deemed to be University,Department of Computer Science and Engineering,Bhubaneswar,India

2. Siksha ‘O’ Anusandhan (Deemed to be University,Department of Computer Application,Bhubaneswar,India

3. Symbiosis Institute of Technology, Symbiosis International (Deemed University),Pune,India

4. KIIT Deemed to be University,School of Computer Engineering,Bhubaneswar,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10481518/10481530/10481658.pdf?arnumber=10481658

Reference15 articles.

1. Hybrid Method for Short-Term Time Series Forecasting Based on EEMD

2. Short-term bitcoin market prediction via machine learning

3. Stock Prediction with Stacked-LSTM Neural Networks

4. A hybrid model combining variational mode decomposition and an attention-GRU network for stock price index forecasting

5. Stock movement prediction via gated recurrent unit network based on reinforcement learning with incorporated attention mechanisms

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