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Predicting Cryptocurrency Prices Model Using a Stacked Sparse Autoencoder and Bayesian Optimization
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Author(s): S. Baranidharan (CHRIST University (Deemed), India), Raja Narayanan (Dayananda Sagar University, India)and V. Geetha (Seshadripuram Evening College, India)
Copyright: 2023
Pages: 18
Source title:
Revolutionizing Financial Services and Markets Through FinTech and Blockchain
Source Author(s)/Editor(s): Kiran Mehta (Chitkara Business School, Chitkara University, India), Renuka Sharma (Chitkara Business School, Chitkara University, India)and Poshan Yu (Soochow University, China & European Business Institute, Luxembourg & Australian Studies Centre, Shanghai University, China)
DOI: 10.4018/978-1-6684-8624-5.ch005
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Abstract
In recent years, digital currencies, also known as cybercash, digital money, and electronic money, have gained significant attention from researchers and investors alike. Cryptocurrency has emerged as a result of advancements in financial technology and has presented a unique opening for research in the field. However, predicting the prices of cryptocurrencies is a challenging task due to their dynamic and volatile nature. This study aims to address this challenge by introducing a new prediction model called Bayesian optimization with stacked sparse autoencoder-based cryptocurrency price prediction (BOSSAE-CPP). The main objective of this model is to effectively predict the prices of cryptocurrencies. To achieve this goal, the BOSSAE-CPP model employs a stacked sparse autoencoder (SSAE) for the prediction process and resulting in improved predictive outcomes. The results were compared to other models, and it was found that the BOSSAE-CPP model performed significantly better.
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