Next-Day Bitcoin Price Forecast Based on Artificial intelligence Methods

Paper

Next-Day Bitcoin Price Forecast Based on Artificial intelligence Methods

Published Jun 21, 2021 · Liping Yang

ArXiv

UNKNOWN SJR score

2

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0

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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.

Preprint

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.

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Paper

Next-Day Bitcoin Price Forecast Based on Artificial intelligence Methods

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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.

Predicting the Price of Bitcoin Using Hybrid ARIMA and Machine Learning

Hybrid ARIMA and machine learning methods improve the prediction of Bitcoin price, with RMSE and MAPE showing improved accuracy.

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.

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.

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.

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.