Cryptocurrencies are a digital way of money in which all transactions are held electronically. It is a soft currency which doesn’t exist in the form of hard notes physically. Here, we are emphasizing the difference of fiat currency which is decentralized that without any third-party intervention all virtual currency users can get the services. However, getting services of these cryptocurrencies impacts on international relations and trade, due to its high price volatility.
There are several virtual currencies such as bitcoin, ripple, ethereum, ethereum classic, lite coin, etc. In our study, we especially focused on a popular cryptocurrency, i.e., bitcoin. From many types of virtual currencies, bitcoin has a great acceptance by different bodies such as investors, researchers, traders, and policy-makers.
To the best of our knowledge, our target is to implement the efficient deep learning-based prediction models specifically long short-term memory (LSTM) and gated recurrent unit (GRU) to handle the price volatility of bitcoin and to obtain high accuracy. Our study involves comparing these two time series deep learning techniques and proved the efficacy in forecasting the price of bitcoin.
Researches on the prediction of cryptocurrencies using machine learning are not much enough, especially on deep learning models. According to the research of 2016, more than 600 papers have been published on this topic. Our literature survey covers work done on bitcoin (BTC) price prediction using different techniques, the need, and evaluation of recurrent neural network (RNN) and its system architecture.
The proposed methodology considers two different deep learning-based prediction models to forecast daily price of bitcoin by identifying and evaluating relevant features by the model itself. After applying both the models for bitcoin prediction, we can determine which model is much more accurate for the future fulfillment of our target and select appropriate parameters to obtain a better performance. In this work, we have proposed deep learning mechanisms such as LSTM and GRU which are the latest and efficient techniques for the forecasting of bitcoin price. As bitcoin is the most popular cryptocurrency, the price volatility issue should be handled within a short period of time.
RNN is a deep neural network characterized as a recurrent connection between the input and output of its neurons or layers and capable of learning sequences designed to capture temporal contextual information along time series data. They have recently gained popularity in deep learning due to their ability to overcome the limitation of existing neural network architecture where it comes to learn over long sequences. Two common RNN networks are LSTM and GRU and presented in the subsequent sections.
LSTMs are explicitly designed to avoid the long-term dependency problem. Remembering information for long periods of time is practically their default behavior, not something they struggle to learn. All recurrent neural networks have the form.
Simulation Results and Analysis
Data and Data Set Preparation Method
Data preparation is the process of collecting, combining, organizing, and structuring data, and then it can be considered as data visualization, analytics, and data mining with machine learning applications. It is critical to feed accurate data for the problem we want to solve.
Data set preparation is a crucial step in machine learning. As we mentioned before, the data preparation impacts the accuracy of the predictions. Therefore, in this section, we should explain the details of the data sets. We will expose the methods used to prepare the data in scope of our model.
The dataset used for this research consists of daily price value collected from Kaggle website https://www.kaggle.com. The overall data collection period is from January 1, 2014 to February 20, 2018. In this dataset, there are seven attributes such as opening price, high price, low price, and closing prices and also the market cap of publicly traded outstanding shares.
Results and Discussion
The proposed model of LSTM and GRU price prediction of bitcoin was trained, and the predictions were carried out for popular cryptocurrency. The accuracy of the proposed LSTM as well as GRU model is investigated by finding the root mean square error (RMSE) and mean absolute percentage error (MAPE) to determine which model has better accuracy. We observed from the resultant Table 1 that LSTM takes greater compilation time than GRU model. The MSE value obtained for 7 days ahead from both the models is plotted and shown in and it is clearly observed that GRU is converging faster and steady than the LSTM model.it is discovered that the variation of actual price and predicted price is more in LSTM than the GRU.
Conclusion and Future Work
Bitcoin is the most popular decentralized way of virtual currency which has a great role in the free market economy and avoids the intermediary of another third party between customers. The main objective of our study is to forecast the bitcoin price with improved efficiency using deep learning models and minimizing the risks for the investors as well as policy-makers. We have implemented two deep learning techniques such as LSTM and GRU as prediction models.
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