Traditional time series modeling techniques emphasize on predicting cryptocurrencies using classically structured data representation as numerical features to present the time-series datasets. In this paper, a novel approach to analyze time-series data charts using a modified Convolutional Neural Networks (CNNs) is proposed. The CNNs have been adopted to recognize subtle and undetectable patterns within images of time-series data charts. Our approach has been proven to achieve significant results, suggesting a need for further research into this new method for time series modeling, especially for Bitcoin.
In the past eight years of Bitcoin's history, the economy has seen the price of Bitcoin rapidly grow due to its promising outlook on the future for crypto currencies. Investors have taken note of several advantages Bitcoin provides over the traditional banking system. One such trait is that Bitcoin allows for decentralized banking, meaning that Bitcoin cannot be regulated by powerful banks. There is also a market cap of 21 million Bitcoins that can be in circulation, therefore a surplus of Bitcoins cannot be "printed" which would result in inflation. Bitcoin resolves the issue with transaction security by using a block chain, or a ledger, which records the history of every transaction ever made into one long hexadecimal "chain" of anonymous transactions, which keeps transaction history transparent, but also confidential. Bitcoin as a result has become a very bullish investing opportunity, and due to the huge volatility of the Bitcoin market price, this paper attempts to aid in investment decision making by providing Bitcoin market price prediction. Our team explored several Machine Learning algorithms by using Supervised Learning to train a prediction model and provide informative analysis of future market prices. We start with Linear Regression models, and train on several important features, then We proceed with the implementation of Recurrent Neural Networks (RNN) with Long Short Term Memory (LSTM) cells. All code is written in Python using Google's Tensor Flow software library. We show that the price of Bitcoin can be predicted with Machine Learning with high degree of accuracy.
Alberto Femenias-Hermida, Cristian R. Munteanu, José M. Vázquez-Naya
One of the major factors hindering the adoption of crypto assets in general, and Bitcoin in particular, is the high level of complexity they present to the common user. Although physical coins are a possible solution, the need to place trust in the manufacturers (so that they throw away the private key) is a big drawback that has hampered their widespread use. The recent boom of the maker movement has brought in a significant number of users with access to 3D printing devices, as well as the supporting electronic and computing resources. We have taken advantage of these capabilities to develop an open source project that interested parties can use to easily print a physical model of a Bitcoin coin, along with the necessary software that allows the creation and validation of keys and addresses.
The blockchain is significantly adopted widely in all domains due to its open , decentralized , distributed form of public digital ledger where all transactions are put down between people across many nodes, so that the record cannot be made diverse retrospectively without the adaptation of all successive blocks and the consensus of the network infrastructure. Here we emphasize the beauty of blockchain which is regarded as a growing chain of records fastened by the robust cryptography technique. Cryptography rooted upon a written code that needs authorized decoding and standard format of encryption. Blockchain is governed by a network that sticks fast to protocols. It would bring off nodal communication and validation of new blocks. Miners can validate transactions, and a transaction log is to be inscribed into the blockchain. Mining step clings to the application of an algorithm to validate and retrieve data. Cryptocurrency is an innovation in digi-currency that is digital by nature in which encryption is involved for the regulation and generation of units of currency. Cryptocurrency uses cryptography for data security and blockchain technology for making transactions. This mechanism of adding valid transactions to a chain of records in its entirety is remarked as a blockchain algorithm. In blockchain, every node in the network culminates in the same conclusion; each one is capable of updating the record independently, with the most promising record conceptualized as the de-facto official record in lieu of a master copy. Specific transactions broadcast this message, and every node build’s its own updated version of events. This tremendous application makes enigmatic blockchain technology totally unique. It is regarded as a revolution in recording and distributing information, because it eliminates the need of a third party to accelerate digital relationships. Blockchain technology is extensively designed to meet in various emerging aspects. It is built using emerging protocols that provide right framing for a peer-to-peer network, which is a pile of records, based on private key cryptography to verify agents’ identification. Algorithms play a vital role in the blockchain technology. A blockchain is a series of transactional interactions that do not need a trusted third-party verification for authenticity. The marshaling of digital relationships is secured by an inherent mechanism. Subsequently, it makes the application robust, simple, and sophisticated. Considering its most popular and successful application, Bitcoin is a cryptocurrency that constructs upon a consensus network algorithm called proof of work . Bitcoin was the front release of an electronic money system that assembles a peer-to-peer network to prevent double spending and validate transactions. It is completely decentralized without any central authority. In this chapter, we focus upon blockchain algorithm and its impact upon cloud-connected ecosystem.
Cryptocurrency market has a potential growth since Bitcoin emerged as one of the commodity investment in Indonesia. Besides Bitcoin, there is another altcoin which rapidly developing and dominating the cryptocurrency market, Ethereum. Therefore, this study aims to analyze the factors that affect Ethereum prices through macroeconomic aspects, such as EUR/USD exchange rate and the price of gold, as well as Bitcoin and other altcoins prices in the cryptocurrency market. The time series data consists of weekly data of all variables will be utilized during the 2016-2018 period. This study will conduct an empirical analysis by using the Autoregressive Distributed Lag (ARDL) test model. The result found that only in the short term, EUR/USD affects the Ethereum prices, while the price of gold do not show any effect on Ethereum prices. Moreover, Bitcoin and 2 alt-coins (Litecoin, and Monero) affect significantly the Ethereum prices. Nevertheless, Ripple and Stellar do not show significant effect on Ethereum prices.
As Ethereum's smart contracts have boomed, it has become an integral part of the blockchain ecosystem. Unfortunately, some malicious users also find the opportunity to use fraudulent means to profit. A new reported approach is to lure new users or other attackers into the contract in an attempt to make a profit by exposing seemingly obvious flaws in the contract. But in fact, the contract contains a hidden trap that ultimately benefits the creator of the contract. Such contracts are known as honeypot contracts in the blockchain ecosystem. Previous studies proposed two methods to identify such smart contracts by using symbolic execution and contract behaviors. However, these methods either make it difficult to discover new categories or fail to warn users before they lose money. To solve this problem, we propose a machine learning model to detect honeypot contracts based on N-gram features and LightGBM. Extensive experiments show that our proposed model performs well in different conditions.
Uğur Kaya, Fırat Akba, İ̇hsan Tolga Medeni, Tunç D. Medeni
Son zamanlarda kullanımı oldukça yaygınlaşan blokzinciri teknolojisinin, İnternet teknolojisi ile beraber adı sıkça anılır olmaya başlamıştır. Blokzinciri teknolojisiyle geliştirilen Bitcoin, sanal para birimleri arasında en çok piyasa hacmini elinde bulunduran sanal para birimidir. Sanal para piyasalarının kontrolünü elinde bulunduran bir merkezi otoritenin olmaması sebebiyle fiyat manipülasyonlarına ve dışarıdan müdahalelere açık olan bu pazarda, en uçtaki yatırımcının yatırım yapabilmesi açısından yol gösterimine ihtiyaç duyulmaktadır. Son zamanlarda bu ihtiyacı karşılamak amacıyla birtakım yöntemler kullanılmaya başlanmıştır. Bu çalışmada makine öğrenmesi, zaman serileri analizi ve derin öğrenme yöntemleri kullanılarak Bitcoin fiyatlarındaki dalgalanma hakkında çeşitli tahminleme ve sınıflama yöntemleri beraber olarak değerlendirilmiştir. Bu bağlamda, koronavirüs pandemisi öncesi ve sonrasındaki Bitcoin kapanış fiyatları ve düşüş-yükseliş eğilimleri baz alınarak iki ayrı veri kümesi oluşturulmuştur. Bu veri kümeleri üzerinde tahmin ve sınıflama yöntemleri değerlendirilerek, başarıları karşılaştırılmıştır. Karşılaştırmalar sonucunda, pandemi öncesi verilerle yapılan çalışmada Destek Vektör Makineleri, pandemi sonrası verilerle yapılan çalışmada ise ARIMA en başarılı sonuçları vermiştir.
The provision of electric vehicles (EVs) is increasing due to the need for ecological green energy. The increment in EVs leads to an intelligent electric vehicle transportation system's need instead of cloud-based systems to manage privacy and security issues. Collecting and delivering the data to current transportation systems means disclosing personal information about vehicles and drivers. We have proposed a secure and intelligent electric vehicle transportation system based on blockchain and machine learning. The proposed method utilizes the state of the art smart contract module of blockchain to build an inference engine. This system takes the sensors' data from the vehicle control unit of EV, stores it in the blockchain, makes decisions using an inference engine, and executes those decisions using actuators and user interface. We have utilized a double-layer optimized long short term memory (LSTM) algorithm to predict EV's stator temperature. We have also performed an informal analysis to demonstrate the proposed system's robustness and reliability. This system will resolve the security issues for both information and energy interactions in EVs.
The top priority of today's healthcare system is delivering medicine directly from the manufacturer to end-user. The pharmaceutical supply chain involves some level of commingling of a collection of stakeholders such as distributors, manufacturers, wholesalers, and customers. The biggest challenge associated with this supply chain is temperature monitoring as well as counterfeit drug prevention. Many drugs and vaccines remain viable within a specific range of temperatures. If exposed beyond this temperature range, the medicine no longer works as intended. In this paper, an Internet of Things (IoT) sensor-based blockchain framework is proposed that tracks and traces drugs as they pass slowly through the entire supply chain. On the one hand, these new technologies of blockchain and IoT sensors play an essential role in supply chain management. On the other hand, they also pose new challenges of security for resource-constrained IoT devices and blockchain scalability issues to handle this IoT sensor-based information. In this paper, our primary focus is on improving classic blockchain systems to make it suitable for IoT based supply chain management, and as a secondary focus, applying these new promising technologies to enable a viable smart healthcare ecosystem through a drug supply chain.
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.
Ramon Gomes da Silva, Matheus Henrique Dal Molin Ribeiro, Naylene Fraccanabbia, Viviana Cocco Mariani · 5 authors
Bitcoin is the leading currency in the cryptocurrency market capturing attention worldwide. Forecasting the Bitcoin price as accurate as possible is essential, but due to its high volatility this task is challenging. Many researchers try, through the years, to develop efficient models for predicting the Bitcoin price using several different data-driven approaches. The objective of this paper is to develop a novel decomposition-ensemble learning model that combines Variational Mode Decomposition (VMD) and Stacking-ensemble learning (STACK) with machine learning algorithms to forecast the Bitcoin price multi-step ahead. The algorithms are k-Nearest Neighbors, Support Vector Regression with Linear kernel, Feed-forward Artificial Neural Network with single-layer perceptron, Generalized Linear Model, and Cubist. Correlation matrix (CORR), principal component analysis (PCA), and Box-Cox transformation (BOXCOX) were used as data preprocessing techniques. Estimating the performance of the proposed models (namely VMD-STACK-CORR, VMD-STACK-PCA, and VMD-STACK-BOXCOX) using relative root mean square error, symmetric mean absolute percentage error, and absolute percentage error measures, defined that for one-day-ahead forecast VMD-STAK-BOXCOX model presented the better performance, and for two and three-days-ahead forecast VMD-STACK-CORR model was chosen, compared to VMD, STACK, and machine learning algorithms models' performance. Diebold-Mariano statistical test was conducted to evaluate a reduction in forecasting errors. Therefore, the proposed models (VMD-STACK-CORR, VMD-STACK-PCA, and VMD-STACK-BOXCOX) indeed forecast accurately Bitcoin price and outperformed the compared models (VMD, STACK, and machine learning models).
Blockchain technology has become a catchword in the recent technological era. It is used in industry as well as by researchers. Blockchain technology concept was used first time in bitcoin application and now its application areas are expanding through all directions in the digital world. Blockchain technology has characteristics as transparency, decentralization, persistency, auditability. There are pros and cons of these characteristics. Based on the advantages, the various domains are adopting the blockchain technology for the development of applications. Different industries and governments are developing their projects using blockchain technology to fulfil their goals as they are getting solutions to their problems through this technology. For developing the applications, various platforms are introduced. These development platforms support by various feature and there is an add-on in that features as per requirements come up. Toward the end of the discussion, the paper is enlightening the challenges in blockchain technology. As the demand of the technology increases, the issues are raising and attackers are attracting to break the system and find out the loopholes in that. The report mentioned the challenges with examples which will be pointing towards various areas of the research.
Agriculture and livestock play a vital role in social and economic stability. Food safety and transparency in the food supply chain are a significant concern for many people. Internet of Things (IoT) and blockchain are gaining attention due to their success in versatile applications. They generate a large amount of data that can be optimized and used efficiently by advanced deep learning (ADL) techniques. The importance of such innovations from the viewpoint of supply chain management is significant in different processes such as for broadened visibility, provenance, digitalization, disintermediation, and smart contracts. This article takes the secure IoT-blockchain data of Industry 4.0 in the food sector as a research object. Using ADL techniques, we propose a hybrid model based on recurrent neural networks (RNN). Therefore, we used long short-term memory (LSTM) and gated recurrent units (GRU) as a prediction model and genetic algorithm (GA) optimization jointly to optimize the parameters of the hybrid model. We select the optimal training parameters by GA and finally cascade LSTM with GRU. We evaluated the performance of the proposed system for a different number of users. This paper aims to help supply chain practitioners to take advantage of the state-of-the-art technologies; it will also help the industry to make policies according to the predictions of ADL.
Heshan Sameera Kankanam Pathiranage, Huilin Xiao, Weifeng Li
Cryptocurrency is an emerging phenomenon set to revolutionize the financial industry. It utilizes blockchain technology, which ensures data is immutable, transparent and reliable. Cryptocurrency is present in different forms like Blockchain and is present in most parts of the world. Developed nations have a significant interest in technology and have put a measure to investigate while developing nations are also following suit. The global interest in cryptocurrency is associated with the fact that the movement of money will be traceable and it will minimize activities such as money laundering and corruption. The paper analyzes a selected period of six years to establish the efficiency of the Blockchain in the markets. A graph and tables are essential in generating more data for interpretation of the events. The tests used in the analysis are the Ljung-box, R/S Hurst, BDS, Runs, AVR test, and Bartels tests. All the tests demonstrated inefficiencies in the Bitcoin trend, especially in the full sample and slightly low inefficiency in the later subsample. The conclusion made is that bitcoin will improve its efficiency over time as it is an emerging industry that needs to grow. Developing countries experience higher inefficiency combined with the prevalent policy challenges and issues like corruption and unemployment.
Haotian Yang, Shuming Xiong, Samuel Akwasi Frimpong, Mingzheng Zhang
The introduction of a consortium blockchain-based agricultural machinery scheduling system will help improve the transparency and efficiency of the data flow within the sector. Currently, the traditional agricultural machinery centralized scheduling systems suffer when there is a failure of the single point control system, and it also comes with high cost managing with little transparency, not leaving out the wastage of resources. This paper proposes a consortium blockchain-based agricultural machinery scheduling system for solving the problems of single point of failure, high-cost, low transparency, and waste of resources. The consortium blockchain-based system eliminates the central server in the traditional way, optimizes the matching function and scheduling algorithm in the smart contract, and improves the scheduling efficiency. The data in the system can be traced, which increases transparency and improves the efficiency of decision-making in the process of scheduling. In addition, this system adopts a crowdsourcing scheduling mode, making full use of idle agricultural machinery in the society, which can effectively solve the problem of resource waste. Then, the proposed system implements authentication access mechanisms, and allows only authorized users into the system. It includes transactions based on digital currency and eliminates third-party platform to charge service fees. Moreover, participating organizations have the opportunity to obtain benefits and reduce transaction costs. Finally, the upper layers supervision improves the efficiency and security of consensus algorithm, allows supervisors to block users with malicious motives, and always ensures system security.
Murat Osmanoğlu, Bülent Tuğrul, Tuncay Dogantuna, Erkan Bostancı
Price fluctuation in agricultural products that adversely affects the actors of the market is a serious issue influenced by many factors. Uncertainty in yield estimation of the products can be counted as one of the major factors. It is a fact that an effective yield estimation method may help decision-making actors to develop effective and stable production and price policies. There are traditional and remote sensing methods aimed at calculating the correct yield estimate. However, these methods produce outputs only after the sowing season of the products. In this case, decision makers do not have enough instruments in hand to ensure price stability. Generally, the existing yield estimation systems are designed in a centralized setting. However, effective tracking may not be possible due to problems in the data flow. In this article, we will propose a blockchain-based solution that carries out yield estimation of agricultural products. Our solution brings all participants interested in agriculture together and produces an early yield estimation. Thus, the necessary precautions for the excessive imbalances that may arise in agricultural products will be planned in advance.
Purpose The encrypted money market has attracted the attention of investors all over the world. Among the encrypted currency, bitcoin is undoubtedly the most popular. Because blockchain technology is the crucial support of bitcoin, exploring the relationship between bitcoin and the blockchain index is necessary. Design/methodology/approach This paper uses the Granger causality test to explore the correlation between bitcoin and the blockchain index. Furthermore, their volatility is analyzed by a GARCH-class model. Findings The results show that no significant correlation exists between bitcoin and the blockchain index; external shocks aggravate the volatility of bitcoin and the blockchain index, and the volatility has a certain degree of sustainability; and blockchain index has obvious leverage, namely, its decline has a stronger impact. Originality/value The volatility of bitcoin and the blockchain index is crucial for investors.
Eng Chuen Loh, Shuhaida Ismail, Azme Khamis, Aida Mustapha
Bitcoin is the most popular cryptocurrency with the highest market value. It was said to have potential in changing the way of trading in future. However, Bitcoin price prediction is a hard task and difficult for investors to make decision. This is caused by nonlinearity property of the Bitcoin price. Hence, a better forecasting method are essential to minimize the risk from inaccuracy decision. The aim of this paper is to compare two different training algorithms which are Levenberg-Marquardt (LM) backpropagation algorithm and Scaled Conjugate Gradient (SCG) backpropagation algorithm using Feedforward Neural Network (FNN) to forecast the Bitcoin price. After obtaining the forecasting result, forecast accuracy measurement will be carried out to identify the best model to forecast Bitcoin price. The result showed that the performance of Bitcoin price forecasting increased after the application of FNN – LM model. It is proven that Levenberg-Marquardt backpropagation algorithm is better compared to Scaled Conjugate Gradient backpropagation when forecasting Bitcoin price using FNN. The resulting model provides new insights into Bitcoin forecasting using FNN – LM model which directly benefits the investors and economists in lowering the risk of making wrong decision when it comes to invest in Bitcoin. Keywords: Bitcoin Price; Artificial Neural Network; Forecasting