Paper
Next-Day Bitcoin Price Forecast Based on Artificial intelligence Methods
Published Jun 21, 2021 · Liping Yang
ArXiv
UNKNOWN SJR score
2
Citations
0
Influential Citations
Abstract
In recent years, Bitcoin price prediction has attracted the interest of researchers and investors. However, the accuracy of previous studies is not well enough. Machine learning and deep learning methods have been proved to have strong prediction ability in this area. This paper proposed a method combined with Ensemble Empirical Mode Decomposition (EEMD) and a deep learning method called long short-term memory (LSTM) to research the problem of next-day Bitcoin price forecast.
Study Snapshot
Key takeawayThis paper proposes a method combining Ensemble Empirical Mode Decomposition (EEMD) and long short-term memory (LSTM) for accurate next-day Bitcoin price forecasting.
PopulationOlder adults (50-71 years)
Sample size24
MethodsObservational
OutcomesBody Mass Index projections
ResultsSocial networks mitigate obesity in older groups.
Sign up to use Study Snapshot
Consensus is limited without an account. Create an account or sign in to get more searches and use the Study Snapshot.
Full text analysis coming soon...
References
Long-range dependence, multi-fractality and volume-return causality of Ether market.
This paper explores the Ether market using detrended fluctuation analysis and asymmetric multifractal detrended fluctuation analysis, revealing unique properties and causality between investor activity and returns.
2020·16citations·Qing Han et al.·Chaos
Chaos
Next-Day Bitcoin Price Forecast
ARIMA models outperform NNAR models in volatile Bitcoin price prediction, with ARIMA outperforming NNAR in both training and test samples.
2019·77citations·Z. H. Munim et al.·Journal of Risk and Financial Management
Journal of Risk and Financial Management
Machine learning: Trends, perspectives, and prospects
Machine learning is rapidly expanding, transforming decision-making in various fields by improving computers through experience and utilizing online data and low-cost computation.
2015·5825citations·Michael I. Jordan et al.·Science
Science
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
The empirical mode decomposition method efficiently analyzes complex data sets, revealing imbedded structures and eliminating the need for spurious harmonics in nonlinear and non-stationary data representation.
1998·18429citations·Norden E. Huang et al.·Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences
Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences
Citations
Cryptocurrency Price Prediction Using Deep Learning
Deep learning algorithms using LSTM architecture can predict the price of bitcoin for the next N days, considering various parameters that affect the cryptocurrency's value.
2022·4citations·Tamara Zuvela et al.·2022 IEEE 16th International Symposium on Applied Computational Intelligence and Informatics (SACI)
2022 IEEE 16th International Symposium on Applied Computational Intelligence and Informatics (SACI)