A Gated Recurrent Unit Approach to Bitcoin Price Prediction

@article{Dutta2019AGR,
  title={A Gated Recurrent Unit Approach to Bitcoin Price Prediction},
  author={Aniruddha Dutta and Saket Kumar and Meheli Basu},
  journal={arXiv: Pricing of Securities},
  year={2019}
}
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… 
ETH analysis and predictions utilizing deep learning
TLDR
This paper utilizes deep learning algorithms to predict the closing price of the Ethereum cryptocurrency in a short period through a Convolutional Neural Network and four types of Recurrent Neural Network.
Deep Learning Methods for Modeling Bitcoin Price
TLDR
A comparison of deep learning methodologies for forecasting Bitcoin price and a new prediction model with the ability to estimate accurately is presented, which offers high and stable success results for a future prediction horizon, something useful for asset valuation of cryptocurrencies like Bitcoin.
A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data
  • Qi Zhao
  • Computer Science, Economics
    ArXiv
  • 2020
TLDR
This study shows that the LSTM model could extract universal features from trade-by-trade data, as the learned parameters well maintain their high performance on other cryptocurrency instruments that were not included in training data.
Enhancing Bitcoin Price Fluctuation Prediction Using Attentive LSTM and Embedding Network
TLDR
This paper investigates the Bitcoin price fluctuation prediction problem, which can be described as whether Bitcoin price keeps or reversals after a large fluctuation, and evaluates three kinds of features, including basic features, traditional technical trading indicators, and features generated by a Denoising autoencoder.
Time-Series Prediction of Cryptocurrency Market using Machine Learning Techniques
TLDR
ARIMA is considered as the best model for forecasting Bitcoin price in the crypto-market with RMSE score of 322.4 and MAE score of 227.3.
Short-Term Bitcoin Market Prediction via Machine Learning
TLDR
This work analyzes the predictability of the bitcoin market across prediction horizons ranging from 1 to 60 minutes and finds that, while all models outperform a random classifier, recurrent neural networks and gradient boosting classifiers are especially well-suited for the examined prediction tasks.
Forecasting cryptocurrency price using convolutional neural networks with weighted and attentive memory channels
TLDR
Inspired by the strong correlations among cryptocurrencies and the powerful modelling capability displayed by deep learning techniques, a Weighted & Attentive Memory Channels model is proposed to predict the daily close price and the fluctuation of cryptocurrencies.
Stock Price Forecasting with Deep Learning: A Comparative Study
The long short-term memory (LSTM) and gated recurrent unit (GRU) models are popular deep-learning architectures for stock market forecasting. Various studies have speculated that incorporating
Cryptocurrency direction forecasting using deep learning algorithms
Recently, the deep learning architecture has been used with an increasing rate for forecasting in financial markets. In this paper, the LSTM model is used to forecast the daily closing price
Review on Bitcoin Price Prediction Using Machine Learning and Statistical Methods
Bitcoin is invented in 2009 by the pseudonymous Satoshi Nakamoto. Bitcoin is a decentralized digital currency system [1]. Bitcoin is the most acknowledged cryptocurrency in the world, which provide
...
1
2
3
...

References

SHOWING 1-10 OF 113 REFERENCES
Bitcoin price prediction using machine learning: An approach to sample dimension engineering
TLDR
This investigation of Bitcoin price prediction can be considered a pilot study of the importance of the sample dimension in machine learning techniques, with accuracy reaching 67.2%.
Predicting the Price of Bitcoin Using Machine Learning
  • S. Mcnally, Jason Roche, Simon Caton
  • Computer Science
    2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)
  • 2018
TLDR
The goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD can be predicted through the implementation of a Bayesian optimised recurrent neural network (RNN) and a Long Short Term Memory (LSTM).
Predicting short-term Bitcoin price fluctuations from buy and sell orders
TLDR
A generative temporal mixture model of the volatility and trade order book data is constructed, which is able to out-perform the current state-of-the-art machine learning and time-series statistical models.
Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies
TLDR
A systematic analysis of the use of deep learning networks for stock market analysis and prediction using five-minute intraday data from the Korean KOSPI stock market as input data to examine the effects of three unsupervised feature extraction methods.
Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders
TLDR
This paper proposes temporal mixture models capable of adaptively exploiting both volatility history and order book features, and demonstrates the prospect of the temporal mixture model as an interpretable forecasting framework over heterogeneous Bitcoin data.
Automated Bitcoin Trading via Machine Learning Algorithms
TLDR
This project attempts to apply machine-learning algorithms to predict Bitcoin price by modeling the price prediction problem as a binomial classification task, experimenting with a custom algorithm that leverages both random forests and generalized linear models.
Forecasting Economics and Financial Time Series: ARIMA vs. LSTM
TLDR
The empirical studies conducted and reported in this article show that deep learning-based algorithms such as LSTM outperform traditional-based algorithm such as ARIMA model and the average reduction in error rates obtained by L STM is between 84 - 87 percent when compared to ARimA indicating the superiority of LSTm to ARIMa.
A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index
TLDR
This paper explores the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVM) to build prediction models for the S&P 500 stock index and shows how traditional models such as multiple linear regression (MLR) behave in this case.
Can We Forecast Daily Oil Futures Prices? Experimental Evidence from Convolutional Neural Networks
TLDR
This study confirms that artificial intelligence (AI)-based deep-learning approaches can provide more accurate forecasts of short-term oil prices than those of the benchmark Naive Forecast model and provides strong evidence that CNN models with matrix inputs are better atShort-term prediction than neural network (NN) models with single-vector input.
The use of data mining and neural networks for forecasting stock market returns
TLDR
An information gain technique used in machine learning for data mining to evaluate the predictive relationships of numerous financial and economic variables is introduced and shows that the trading strategies guided by the classification models generate higher risk-adjusted profits than the buy-and-hold strategy.
...
1
2
3
4
5
...