Predicting the Price of Bitcoin Using Machine Learning

Mcnally, Sean; Roche, Jason T.; Caton, Simon

doi:10.1109/pdp2018.2018.00060

Cited by 451 publications

(265 citation statements)

References 20 publications

Supporting

Mentioning

209

Contrasting

Unclassified

Order By: Relevance

“…Further, a supervised machine learning algorithm is used by [11] to uncover Bitcoin anonymity using a method for predicting the type of yet-unidentified entities. In [2], data mining techniques are used to implement and train a classifier to identify Ponzi schemes in the Bitcoin blockchain and in [18] a Bayesian optimized recurrent neural network (RNN) and a Long Short Term Memory (LSTM) are implemented to predict the direction of Bitcoin price in USD.…”

Section: Related Workmentioning

confidence: 99%

Cascading Machine Learning to Attack Bitcoin Anonymity

Zola

Eguimendia

Bruse

et al. 2019

2019 IEEE International Conference on Blockchain (Blockchain)

View full text Add to dashboard Cite

Bitcoin is a decentralized, pseudonymous cryptocurrency that is one of the most used digital assets to date. Its unregulated nature and inherent anonymity of users have led to a dramatic increase in its use for illicit activities. This calls for the development of novel methods capable of characterizing different entities in the Bitcoin network.In this paper, a method to attack Bitcoin anonymity is presented, leveraging a novel cascading machine learning approach that requires only a few features directly extracted from Bitcoin blockchain data. Cascading, used to enrich entities information with data from previous classifications, led to considerably improved multi-class classification performance with excellent values of Precision close to 1.0 for each considered class. Final models were implemented and compared using different machine learning models and showed significantly higher accuracy compared to their baseline implementation.Our approach can contribute to the development of effective tools for Bitcoin entity characterization, which may assist in uncovering illegal activities.

show abstract

Section: Related Workmentioning

confidence: 99%

Cascading Machine Learning to Attack Bitcoin Anonymity

Zola

Eguimendia

Bruse

et al. 2019

2019 IEEE International Conference on Blockchain (Blockchain)

View full text Add to dashboard Cite

show abstract

“…Examples of machine learning studies to predict Bitcoin prices include random forests (Madan 2015), Bayesian neural network (Jang 2017), and neural networks (McNally 2018 (Cybenko 1989). There are a number of previous works that have applied artificial neural networks to financial investment problems (Chong 2017, Huck 2010.…”

Section: Introductionmentioning

confidence: 99%

“…However, Pichl and Kaizoji 2017 conclude that although neural networks are successful in approximating bitcoin log return distribution, more complex deep learning methods such as RNNs and LSTM techniques should yield substantially higher prediction accuracy. Some studies have used RNN's and LSTM to forecast Bitcoin pricing in comparison with traditional ARIMA models (McNally 2018. McNally 2018 show that RNN and LSTM neural networks predict prices better than the traditional multilayer perceptron (MLP) due to the temporal nature of the more advanced algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Gated Recurrent Unit Approach to Bitcoin Price Prediction

Dutta

Kumar

Basu

2020

JRFM

137

View full text Add to dashboard Cite

In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. In this study, we investigate a framework with a set of advanced machine learning forecasting methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that gated recurring unit (GRU) model with recurrent dropout performs better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.

show abstract

“…Reference [20] explored several models (RNN, Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM)) to predict Bitcoin prices sourced from the Bitcoin Price Index (BPI). Training was performed using Graphics Processing Unit (GPU) due to the volume of computations required to train the models.…”

Section: Work On Bitcoin Forecastingmentioning

confidence: 99%

“…The first set of inputs were several commonly available data namely the opening, closing, minimum and maximum daily past prices. The second set of inputs consist of several Moving Average (MA) over different intervals (5,10,20,50, 100, 200 days), a commonly used technical indicator for investors to estimate the direction of stock prices. These inputs were then used to train the MLP neural network to predict the next day prices for Bitcoin.…”

Section: Introductionmentioning

confidence: 99%