A Comparative Analysis and Prediction over Bitcoin Price Using Machine Learning Technique (original) (raw)
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Bitcoin Price Prediction and Forecasting
Bitcoin is one of the oldest and biggest cryptocurrencies being traded as of now, in terms of the volume being traded. It is a digital asset over which central banks or any financial Institutions have no control or regulations. Bitcoin has a market share of more than 55% as compared to other cryptocurrencies. It is very sporadic and this is one of the most important reasons which attracted us to analyze and predict its price. Here, we intend to study the prediction of Bitcoin prices using Machine Learning Techniques and prepare a strategy to maximize gains for investors.
Scientia Iranica
Cryptocurrencies, which the Bitcoin is the most remarkable one, have allured substantial awareness up to now, and they have encountered enormous instability in their price. While some studies utilize conventional statistical and econometric ways to uncover the driving variables of Bitcoin's prices, experimentation on the advancement of predicting models to be used as decision support tools in investment techniques is rare. There are many different predicting cryptocurrencies' price methods that cover various purposes, such as forecasting a one-step approach that can be done through time series analysis, neural networks, and machine learning algorithms. Sometimes realizing the trend of a coin in a long run period is needed. In this paper, some machine learning algorithms are applied to find the best ones that can forecast Bitcoin price based on three other famous coins. Second, a new methodology is developed to predict Bitcoin's worth, this is also done by considering different cryptocurrencies prices (Ethereum, Zcash, and Litecoin). The results demonstrated that Zcash has the best performance in forecasting Bitcoin's price without any data on Bitcoin's fluctuations price among these three cryptocurrencies.
Review on Bitcoin Price Prediction Using Machine Learning and Statistical Methods
Sakarya University Journal of Computer and Information Sciences, 2020
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 it interesting for financier. The cryptocurrency market capitalization on date 22nd July 2020 value represents roughly USD 277 billion of dollars, bitcoin representing 62% of it. However, a disadvantage for investors is the difficulty of predicting the price of bitcoin due to the high volatility of the bitcoin exchange rate. Measurement, estimation, and modeling of currency exchange rate volatility compose a significant research area. For this reason, a lot of studies done about bitcoin price prediction both Machine Learning (ML) and Statistical Methods. In comparison studies, ML methods perform better in general. This review is a comprehensive study on how we can better predict bitcoin prices by grouping previously done studies. The presentation of Bitcoin price prediction studies in groups reveals, the difference from other review studies. These are statistical methods, ML and statistical methods, ML-ML, frequency effect of selected time, effect of social media and web search engine, causality, optimization of hyperparameters methods.
BITCOIN PRICE PREDICTION USING MACHINE LEARNING
International Journal of Engineering Technologies and Management Research, 2021
In this paper, we use the LSTM version of Recurrent Neural Networks, pricing for Bitcoin. To develop a better understanding of its price in luence and a common view of this good invention, we irst give a brief overview of Bitcoin again economics. After that, we de ine the database, including data from stock market indices, sentiment, blockchain and Coinmarketcap. Further in this investigation, we demonstrate the use of LSTM structures with the series of time mentioned above. In conclusion, we draw the Bitcoin pricing forecast results 30 and 60 days in advance.
From regression models to machine learning approaches for long term Bitcoin price forecast
Annals of Operations Research
We carry on a long term analysis for Bitcoin price, which is currently among the most renowned crypto assets available on markets other than Forex. In the last decade Bitcoin has been under spotlights among traders all world wide, both because of its nature of pseudo-currency and for the high volatility its price has frequently experienced. Considering that Bitcoin price has earned over five orders of magnitude since 2009, the interest of investors has been increasingly motivated by the necessity of accurately predicting its value, not to mention that a comparative analysis with other assets as silver and gold has been under investigation, too. This paper reports two approaches for a long term Bitcoin price prediction. The first one follows more standard paradigms from regression and least squares frameworks. Our main contribution in this regard fosters conclusions which are able to justify the cyclic performance of Bitcoin price, in terms of its Stock-to-Flow. Our second approach is definitely novel in the literature, and indicates guidelines for long term forecasts of Bitcoin price based on Machine Learning (ML) methods, with a specific reference to Support Vector Machines (SVMs). Both these approaches are inherently data-driven, and the second one does not require any of the assumptions typically needed by solvers for classic regression problems.
A Novel Method for Prediction of Cryptocurrency Price
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
The safe hash method (SHA) 256 and message digest (MD) 5 are used in peer-to-peer transaction arrangements, also known as sophisticated forms of currency, to safeguard data transfers. Prices for Bitcoin are extremely volatile, act erratically, and have reached eccentricity. They are frequently used for initiative and have mostly replaced traditional trading vehicles like metals, bequests, and the stock market. They must be created due to the significance of reliable deciding models in business. However, it is difficult to predict bitcoin's price because it is based on other digital currencies. Bitcoin prices have been evaluated by a variety of researchers using machine learning (ML) and deep learning models, in addition to other tendency-based market processes. Changing the price of one type of encrypted money may influence other encrypted types of money because all digital currencies are in the same category. The researchers combined sentiments from Twitter and other online amusement sites to enhance the effectiveness of the framework. DL-Gues, a robust and solid structure for forecasting computerized cash costs that considers its reliance on other cryptography-based currencies and market sentiment, is inspired by this work. Twitter as well as cost reports from Run, Litecoin, and Bitcoin were used in our investigation of the Run cost premise. To determine whether DL-Gather could be applied to more sophisticated monetary standards, we evaluated the ends for the premise for the cost of Bitcoin-Cash using the price data and tweets of Bitcoin, Litecoin, and Bitcoin.
International Journal of Engineering, 2020
Time Series Forecasting Machine Learning Bitcoin Multivariate Models Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine (SVM) and Random Forest (RF) are proposed and analyzed for modelling and forecasting the Bitcoin price. While some of the proposed models are univariate, the other models are multivariate and as a result, the maximum, minimum and the opening daily price of Bitcoin are also used in these models. The proposed models are applied on the Bitcoin price from December 18, 2019 to March 1, 2020 and their performances are compared in terms of the performance measures of RMSE and MAPE by Diebold-Mariano statistical test. Based on RMSE and MAPE measures, the results show that SVM provides the best performance among all the models. In addition, ARIMA and Bayesian approaches outperform other univariate models where they provide smaller values for RMSE and MAPE.
Predicting The Prices Of Bitcoin Using Data Analytics
2021
The foremost aim of our paper is to predict next-day and any particular month Bitcoin prices with respect to the company as early as possible. To obtain results at the earliest we made our implementation in Apache Spark, a big data tool. We have also utilised one of the widely used machine learning libraries namely pandas for dataset manipulation, and preferred Pyspark since it is the combination of Apache Spark and Python. For investor interaction with our system we have designed a Graphical User Interface (GUI) and named it as ‘PMIST’ with Tkinter which is a Python’s GUI. The result predicted will be seen in the form of line and bar graphs along with a message prompt where right date for doing investments are suggested. By analyzing those graphs, investors can be able to get idea about the future prices and they can take decision to either invest in future or change their investment time. Also a rewarding system is designed for the investors in which we will provide 50% offer in S...
Forecasting of Cryptocurrency Values using Machine Learning
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Bitcoin is a sort of cryptocurrency that has become a popular stock market investment. Many factors have an impact on the stock market. And bitcoin is a sort of cryptocurrency that has been slowly rising in recent years, with occasional severe declines that have had no discernible effect on the stock market. Because of the volatility, a prediction tool for bitcoin on the stock market is required. LSTM (Long Short-Term Memory) is a type of RNN module that was subsequently converted and used by numerous researchers, and it, like RNN, consists of recurrently consistent modules. The strategy and instruments we used to predict Bitcoin on the stock market yahoo finance can also be used to predict the price of cryptocurrencies. In the final section, we draw conclusions and discuss future work.
Journal of Informatics Electrical and Electronics Engineering (JIEEE), A 2 Z Journals, 2023
Due to economic uncertainty and the financial crisis of 2008, a desire for an unregulated currency arose, leading to the invention of Bitcoin. Using a pseudonym called Satoshi Nakamoto, Bitcoin was created in 2009, anonymously or by a group of unknown individuals. Since Bitcoin has been the most valuable cryptocurrency in recent years, its prices have fluctuated dramatically, making it difficult to predict their prices. Investors, businesses, risk managers, and market analysts can all benefit from being able to predict Bitcoin prices. By using the Bitcoin transaction data obtained from the Bitstamp website in this study, several different Machine Learning models are employed to determine the most accurate model for predicting Bitcoin prices. These models are based on 1-minute interval exchange rates in USD from January 1, 2012, to January 8, 2022. Analysis was performed primarily with Python, but it was also used and Hadoop, a distributed data storage and processing framework that uses the map-reduce programming model to allow efficient parallel processing of Big Data. Based on the results of our research, comprising three experiments, autoregressive-integrated moving average (ARIMA) makes the most accurate prediction of Bitcoin prices, with a 95.98% success rate.