A Gated Recurrent Unit Approach to Bitcoin Price Prediction

Abstract

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.

Deep Learning Methods for Modeling Bitcoin Price

“…Furthermore, these methods show some different significant variables. Such is the case of the variables of forum posts, a variable popularly used as a proxy for the level of future demand that Bitcoin could have, although with divergences in previous works regarding its significance to predict the price of Bitcoin, where some works show that this variable is not significant [11,14]. Finally, these methods show another macroeconomic variable that is more significant, in the case of the dollar exchange rate.…”

“…Different methods were applied in the construction of the Bitcoin price prediction model to build a reliable model, which is contrasted with various methodologies used in previous works to check with which technique a high predictive capacity is achieved; specifically, the methods of deep recurrent neural networks, deep neural decision trees, and deep support vector machines, were used. Furthermore, this work attempts to obtain high accuracy, but it is also robust and stable in the future horizon to predict new observations, something that has not yet been reported by previous works [7][8][9][10][11][12][13][14][15], but which some authors demand for the development of these models and their real contribution [9,12].…”

“…In addition, we used 29 independent variables, classified into demand and supply variables, attractiveness, and macroeconomic and financial variables, as possible predictors of the Bitcoin future price ( Table 1). These variables were used throughout the previous literature [1,3,4,14]. The sample is fragmented into three mutually exclusive parts, one for training (70% of the data), one for validation (10% of the data), and the third group for testing (20% of the data).…”

“…Despite the superiority of computational techniques, Felizardo and colleagues [13] showed that ARIMA had a lower error rate than methods, such as random forest (RF), support vector machine (SVM), LSTM, and WaveNets, to predict the future price of Bitcoin. Finally, other works showed new deep learning methods, such as Dutta, Kumar, and Basu [14], who applied both LSTM and the gated recurring unit (GRU) model; the latter showed the best error result, with an RMSE of 0.019. Ji and co-workers [15] predicted the price of Bitcoin with different methodologies such as deep neural network (DNN), the LSTM model, and convolutional neural network.…”

Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019

“…A gated recurrent unit (GRU) [ 37 ] resolves the vanishing gradient problem in a recurrent neural network and uses an update gate and reset gate, as displayed in Fig. 6 (a).…”