Predicting cryptocurrency prices model using a stacked sparse autoencoder and Bayesian optimization

Title

Predicting cryptocurrency prices model using a stacked sparse autoencoder and Bayesian optimization

Creator

Baranidharan S.; Narayanan R.; Geetha V.

Description

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. 2023, IGI Global.

Source

Revolutionizing Financial Services and Markets Through FinTech and Blockchain, pp. 60-77.

Date

2023-01-01

Publisher

IGI Global

Coverage

Baranidharan S., CHRIST University (Deemed), India; Narayanan R., Dayananda Sagar University, India; Geetha V., Seshadripuram Evening College, India

Rights

Restricted Access

Relation

ISBN: 978-166848626-9; 1668486245; 978-166848624-5

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.4018/978-1-6684-8624-5.ch005

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168066754&doi=10.4018%2f978-1-6684-8624-5.ch005&partnerID=40&md5=43c51c753a15df029594b9e7cbd38ab5