Citation map · Study

SZodixStudyAcademic Library

Shelves Open access

Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated

Deep Learning Methods for Modeling Bitcoin Price

11 references·6 citing·54 second hop

FromToDepth

this work references cited by second hop

References

2002Making best use of model evaluations to compute sensitivity indices2,225 cited
2018Statistical and Machine Learning forecasting methods: Concerns and ways forward1,515 cited
2015What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis989 cited
2003Neural network forecasting for seasonal and trend time series987 cited
2017Can volume predict Bitcoin returns and volatility? A quantiles-based approach718 cited
2018Predicting the Price of Bitcoin Using Machine Learning690 cited
2013Measuring firm performance using financial ratios: A decision tree approach430 cited
2020A Gated Recurrent Unit Approach to Bitcoin Price Prediction230 cited
2019A Comparative Study of Bitcoin Price Prediction Using Deep Learning213 cited
2017Deep convolutional recurrent neural network with attention mechanism for robust speech emotion recognition129 cited
2018Bitcoin price prediction using machine learning86 cited

Cited by

2022Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks58 cited
2021Predictions of bitcoin prices through machine learning based frameworks39 cited
2023Digital financial asset price fluctuation forecasting in digital economy era using blockchain information: A reconstructed dynamic-bound Levenberg–Marquardt neural-network approach

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

37 cited

2022A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price35 cited

2021Mathematical Modeling for Financial Analysis of an Enterprise: Motivating of Not Open Innovation27 cited

2024Bubbles in Bitcoin and Ethereum: The role of halving in the formation of super cycles20 cited