Version 1
: Received: 26 March 2024 / Approved: 27 March 2024 / Online: 27 March 2024 (11:55:20 CET)
How to cite:
Dip Das, J.; Thulasiram, R.K.; Thavaneswaran, A. Hybridized Encoder Decoder based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction. Preprints2024, 2024031677. https://doi.org/10.20944/preprints202403.1677.v1
Dip Das, J.; Thulasiram, R.K.; Thavaneswaran, A. Hybridized Encoder Decoder based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction. Preprints 2024, 2024031677. https://doi.org/10.20944/preprints202403.1677.v1
Dip Das, J.; Thulasiram, R.K.; Thavaneswaran, A. Hybridized Encoder Decoder based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction. Preprints2024, 2024031677. https://doi.org/10.20944/preprints202403.1677.v1
APA Style
Dip Das, J., Thulasiram, R.K., & Thavaneswaran, A. (2024). Hybridized Encoder Decoder based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction. Preprints. https://doi.org/10.20944/preprints202403.1677.v1
Chicago/Turabian Style
Dip Das, J., Ruppa K. Thulasiram and Aerambamoorthy Thavaneswaran. 2024 "Hybridized Encoder Decoder based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction" Preprints. https://doi.org/10.20944/preprints202403.1677.v1
Abstract
This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions. To tackle the challenge effectively, a novel hybridized architecture, AE-GRU, integrating the encoder-decoder principle with GRU is designed. The experimentation involves multiple activation functions and hyperparameter tuning. With extensive experimentation and enhancements applied to AE-LSTM, the proposed AE-GRU architecture still demonstrates significant superiority in forecasting the annual prices of volatile financial assets from multiple sectors mentioned above. Thus, the novel AE-GRU architecture emerges as a superior choice for price prediction across diverse sectors and fluctuating market scenarios by extracting important non-linear features of financial data and retaining the long-term context from past observations.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
