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ChainNet: Learning on Blockchain Graphs with Topological Features
[article]
2019
arXiv
pre-print
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We show that standard graph features such as degree distribution of the transaction graph may not be sufficient to capture network dynamics and its potential impact on fluctuations of Bitcoin price. ...
In contrast, the new graph associated topological features computed using the tools of persistent homology, are found to exhibit a high utility for predicting Bitcoin price dynamics. ...
CONCLUSION ChainNet is a price prediction platform that utilizes topological characteristics of a blockchain graph. ...
arXiv:1908.06971v1
fatcat:nh5cmdqrobajfjpjxyno7b2er4
Machine Learning in/for Blockchain: Future and Challenges
[article]
2020
arXiv
pre-print
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In this paper, we review the research on combining blockchain and machine learning technologies and demonstrate that they can collaborate efficiently and effectively. ...
Machine learning and blockchain are two of the most noticeable technologies in recent years. ...
Bitcoin price prediction UTXOs record the number of Bitcoins in transactions, which enables us to track buying and selling information to predict the Bitcoin price. ...
arXiv:1909.06189v3
fatcat:4wxvpsli5bcoldwseem2jpxnue
Assessing Holistic Impacts of Major Events on the Bitcoin Blockchain Network
[article]
2020
arXiv
pre-print
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We have applied our framework to 16 major worldwide events and the Bitcoin blockchain network (defined as Bitcoin transaction and users, blockchain data, and memory pool data) from 2016-2018. ...
Subgroups of these events have strongly consistent temporal impacts on specific facets (e.g. activity or fees) of the Bitcoin ecosystem. ...
[47] created BitIodine, a Bitcoin blockchain analysis framework using user and transaction graphs for forensics analysis. ...
arXiv:2006.02416v1
fatcat:sqkls7gazjbz3fmqm4dtkfhmvu
Analysis of Cryptocurrency Transactions from a Network Perspective: An Overview
[article]
2021
arXiv
pre-print
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Networks are a general language for describing interacting systems in the real world, and a considerable part of existing work on cryptocurrency transactions is studied from a network perspective. ...
This survey aims to analyze and summarize the existing literature on analyzing and understanding cryptocurrency transactions from a network perspective. ...
The transaction model employed by Bitcoin is a transactioncentered model, where a transaction can have multi-inputs and multi-outputs, being associated with multi-addresses. ...
arXiv:2011.09318v2
fatcat:idtd636e75cotgfsgufgu6np3a
Cryptocurrencies and Artificial Intelligence: Challenges and Opportunities
2020
IEEE Access
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ACKNOWLEDGMENT This publication was made possible by a grant from the Qatar National Research Fund (QNRF); project number NPRP X-063-1-014. ...
They derived an entity-transaction graph from the address-transaction graph. ...
[50] used trimmed k-means and k-means clustering based on features from the transactions graph in a semi-supervised way to detect fraudulent activity in the Bitcoin transactions network. ...
doi:10.1109/access.2020.3025211
fatcat:awjyqt5p2veg5ld2lqqm527lgq
A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin
2022
Mathematics
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As the most famous cryptocurrency, the Bitcoin price forecasting model is one of the popular mathematical models in financial technology because of its large price fluctuations and complexity. ...
This paper proposes a novel ensemble deep learning model to predict Bitcoin's next 30 min prices by using price data, technical indicators and sentiment indexes, which integrates two kinds of neural networks ...
[14] proposed a regression framework based on differential evolution to predict bitcoin. ...
doi:10.3390/math10081307
fatcat:cpglcgh6hrfvlaschvb4q5bdoa
Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
2021
Applied Sciences
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Data stored in a blockchain can also be considered to be big data, whereas data mining methods can be applied to extract knowledge hidden in the blockchain. ...
Special attention was paid to anomaly detection and fraud detection, which were identified as the most prolific applications of applying data mining methods on blockchain data. ...
[78] presented a one-class graph deep learning framework for anomaly detection on the Ethereum blockchain. ...
doi:10.3390/app11177987
fatcat:w54qaqlvobfdloqsyqj3lnloyu
Deep Learning-based Cryptocurrency Price Prediction Scheme with Inter-dependent Relations
2021
IEEE Access
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Another advantage of Litecoin is that the transaction fees of its blockchain are lower than many other popular blockchains, including Bitcoin. ...
Guo et al. [76] used a hybrid
model of Multi-scale Residual CNN and LSTM to predict the
closing price of Bitcoin.
) idp 1 = [[p 0 , p 1 , p 2 , ...., p n−1 ], [d 0 , d 1 , d 2 , ...., d n−1 = [[ ...
doi:10.1109/access.2021.3117848
fatcat:xzwoluapozfgfj4uecry534djm
Detecting Mixing Services via Mining Bitcoin Transaction Network with Hybrid Motifs
[article]
2021
arXiv
pre-print
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Specifically, we provide a feature-based network analysis framework to identify statistical properties of mixing services from three levels, namely, network level, account level and transaction level. ...
However, the P2P and pseudonymous nature of Bitcoin make transactions on this platform very difficult to track, thus triggering the emergence of various illegal activities in the Bitcoin ecosystem. ...
Recently, there are many studies utilized network motifs in blockchain transaction network mining tasks, such as price prediction [45] , [46] , network property analysis [47] , exchange pattern mining ...
arXiv:2001.05233v2
fatcat:urjtm7xd3fgbvkpfjraedvrbtq
Return Rate Prediction in Blockchain Financial Products Using Deep Learning
2021
Sustainability
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Recently, bitcoin-based blockchain technologies have received significant interest among investors. They have concentrated on the prediction of return and risk rates of the financial product. ...
The proposed RRP-DLBFP technique involves designing a long short-term memory (LSTM) model for the predictive analysis of return rate. ...
They found that traditional ANNs were 55 percent accurate in forecasting the Bitcoin price. They concluded that Blockchain data on its own has limited predictability. ...
doi:10.3390/su132111901
doaj:a95a273f89b64a72b28d1418838d9cee
fatcat:hvvbzmayujaf7olr64cz6hxdte
Visualization of Blockchain Data: A Systematic Review
2019
IEEE Transactions on Visualization and Computer Graphics
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We present a systematic review of visual analytics tools used for the analysis of blockchains-related data. ...
The blockchain concept has recently received considerable attention and spurred applications in a variety of domains. ...
Based on the survey, the authors developed a general framework for blockchain analytics and showed use cases of analyzing transaction fees and Bitcoin metadata. ...
doi:10.1109/tvcg.2019.2963018
pmid:31899429
fatcat:53tcxbz7sbdszoi4hiwhxcnike
Cryptocurrency Trading: A Comprehensive Survey
[article]
2022
arXiv
pre-print
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This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e.g., cryptocurrency trading systems, bubble ...
and extreme conditions, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). ...
Conclusions We provided a comprehensive overview and analysis of the research work on cryptocurrency trading. This survey presented a nomenclature of the definitions and current state of the art. ...
arXiv:2003.11352v5
fatcat:l7eih2yoazbq5i5lv4wh7c24ha
Cryptocurrency trading: a comprehensive survey
2022
Financial Innovation
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This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e.g., cryptocurrency trading systems, bubble ...
and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). ...
The results showed that standard graph features such as the degree distribution of transaction graphs may not be sufficient to capture network dynamics and their potential impact on Bitcoin price fluctuations ...
doi:10.1186/s40854-021-00321-6
fatcat:d3d2pkxy5fgcfa4s6gi4h2snua
Predicting the Price of Bitcoin Using Machine Learning
2018
2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)
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Jason Roche for accompanying me on this deep journey of mine. I learned a tremendous amount from you in this short space of time. ...
From the graph it is apparent that Bitcoin closing price is correlated with the network hash rate on a long term horizon. ...
(26) analysed the Bitcoin Blockchain to predict the price of Bitcoin using SVM and ANN. The author reported price direction accuracy of 55 percent with a regular ANN. ...
doi:10.1109/pdp2018.2018.00060
dblp:conf/pdp/McNallyRC18
fatcat:3x4xvcae3zautoffrob6r4vkeu
Blockchain Oracle Design Patterns
[article]
2021
arXiv
pre-print
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Transactions are data state changes on the blockchain that are permanently recorded in a secure and transparent way without the need of a third party. ...
Blockchain is a form of distributed ledger technology (DLT) where data is shared among users connected over the internet. ...
A multi-signature address is defined as an address on the blockchain associated with more than one private key, and a multi-signature transaction needs to have more than one private key for transaction ...
arXiv:2106.09349v1
fatcat:jcobgywz2zfgvfragre5qpolc4
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