Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning (original) (raw)
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Journal of Information Security and Applications, 2020
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IJERT-Predicting Price of Cryptocurrency -A Deep Learning Approach
International Journal of Engineering Research and Technology (IJERT), 2021
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IFIP Advances in Information and Communication Technology
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Prediction of Cryptocurrency Price using Time Series Data and Deep Learning Algorithms
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One of the most significant and extensively utilized cryptocurrencies is Bitcoin (BTC). It is used in many different financial and business activities. Forecasting cryptocurrency prices are crucial for investors and academics in this industry because of the frequent volatility in the price of this currency. However, because of the nonlinearity of the cryptocurrency market, it is challenging to evaluate the unique character of timeseries data, which makes it impossible to provide accurate price forecasts. Predicting cryptocurrency prices has been the subject of several research studies utilizing machine learning (ML) and deep learning (DL) based methods. This research suggests five different DL approaches. To forecast the price of the bitcoin cryptocurrency, recurrent neural networks (RNN), long shortterm memories (LSTM), gated recurrent units (GRU), bidirectional long short-term memories (Bi-LSTM), and 1D convolutional neural networks (CONV1D) were used. The experimental findings demonstrate that the LSTM outperformed RNN, GRU, Bi-LSTM, and CONV1D in terms of prediction accuracy using measures such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared score (R 2). With RMSE= 1978.68268, MAE=1537.14424, MSE= 3915185.15068, and R 2 = 0.94383, it may be considered the best method.
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Bitcoin is one of the most popular and valuable cryptocurrencies in the current financial market, attracting traders for investment and thereby opening new research opportunities for researchers. Countless research works have been performed on Bitcoin price prediction with different machine learning prediction algorithms. For the project: relevant features are taken from the dataset having strong correlation with Bitcoin prices and random data chunks are then selected to train and test the model. The random data which has been selected for model training, may cause unfitting outcomes thus reducing the price prediction accuracy. Here, a proper method to train a prediction model is being scrutinised. The proposed methodology is then applied to train a simple Long Short-Term Memory (LSTM) model to predict the bitcoin price for the upcoming 30 days. When the LSTM model is trained with a suitable data chunk, thus identified, sustainable results are found for the prediction. In the end of this project, the work culminates with future improvements. Bitcoin is a kind of Cryptocurrency and now is one of type of investment on the stock market. Stock markets are influenced by many risks of factor. And bitcoin is one kind of cryptocurrency that keep rising in recent few years, and sometimes sudden fall without knowing influence behind it on the stock market. Because it’s fluctuations, there’s a need and automation tool to predict bitcoin on the stock market. This research study learns how to create model prediction bitcoin stock market prediction using LSTM, LSTM (Long Short-Term Memory) is another type of module provided for RNN later developed and popularized by many researchers, like RNN, the LSTM also consists of modules with recurrent consistency. The Method that we apply on this project, also technique and tools to predict Bitcoin on stock market yahoo finance can predict the result above $ 12600 USD for next days after prediction, in the last section we make conclusions and discuss future works.
International Journal of Scientific Research in Science and Technology, 2022
Bitcoin is a form of cryptocurrency that has come to be a famous inventory marketplace funding and it's been gradually growing in current years, and every now and then falling without warning, at the inventory marketplace. Because of its fluctuations, an automatic device for predicting bitcoin at the inventory marketplace is required. However, due to its volatility, traders will want a prediction device to assist them make funding selections in bitcoin or different cryptocurrencies. In this paper, Deep gaining knowledge of mechanisms like Recurrent Neural Network (RNN) and Long short-time period memory (LSTM) is proposed to broaden a version to forecast the bitcoin charge fashion withinside the marketplace. Finally, the predictions end result for the Bitcoin charge fashion are supplied over the subsequent 15, 30, and 60 days. Each version is evaluated in phrases of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) forecasting blunders values. The LSTM version is observed to be the higher mechanism for time-collection cryptocurrency charge prediction, however it takes longer to compile. The goal of this project is to show how a trained machine model can predict the price of a cryptocurrency if we give the right amount of data and computational power. It displays a graph with the predicted values. The most popular technology is the kind of technological solution that could help mankind predict future events. With vast amount of data being generated and recorded on a daily basis, we have finally come close to an era where predictions can be accurate and be generated based on concrete factual data. Furthermore, with the rise of the crypto digital era more heads have turned towards the digital market for investments. This gives us the opportunity to create a model capable of predicting crypto currencies primarily Bitcoin. This can be accomplished by using a series of machine learning techniques and methodologies.
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This study intends to predict the trends of price for a cryptocurrency, i.e. Ethereum based on deep learning techniques considering its trends on time series particularly. This study analyses how deep learning techniques such as multi-layer perceptron (MLP) and long short-term memory (LSTM) help in predicting the price trends of Ethereum. These techniques have been applied based on historical data that were computed per day, hour and minute wise. The dataset is sourced from the CoinDesk repository. The performance of the obtained models is critically assessed using statistical indicators like mean absolute error (MAE), mean squared error (MSE) and root mean squared error (RMSE).
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