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A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model

Xinchen Zhang, Linghao Zhang, Qincheng Zhou, Xu Jin, Shengrong Gong
2022 Computational Intelligence and Neuroscience  
In addition, our LSTM-P model outperforms both the conventional LSTM models and other time series forecasting models in terms of accuracy and precision.  ...  Using the historical price series of Bitcoin and gold from 9/11/2016 to 9/10/2021, we investigate an LSTM-P neural network model for predicting the values of Bitcoin and gold in this research.  ...  Long Short-Term Memory. It was Hochreiter and Schmidhuber who first proposed the long short-term memory (LSTM) neural network in 1997, and it has subsequently gained widespread acceptance.  ... 
doi:10.1155/2022/1643413 pmid:35571687 pmcid:PMC9098287 fatcat:pg6ecx2gzvg2nh2rv4in3nds6e

Predicting the Price of Bitcoin Using Machine Learning

Sean McNally, Jason Roche, Simon Caton
2018 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)  
Jason Roche for accompanying me on this deep journey of mine. I learned a tremendous amount from you in this short space of time.  ...  Thank you for believing in me and making me believe in myself and my abilities. I would also like to thank my girlfriend Katie and my parents for their unfaltering support and counsel.  ...  (RNN) and Long Short Term Memory (LSTM).  ... 
doi:10.1109/pdp2018.2018.00060 dblp:conf/pdp/McNallyRC18 fatcat:3x4xvcae3zautoffrob6r4vkeu

Bitcoin Price Prediction Based on Other Cryptocurrencies Using Machine Learning and Time Series Analysis

Negar Maleki, Alireza Nikoubin, Masoud Rabbani, Yasser Zeinali
2020 Scientia Iranica. International Journal of Science and Technology  
Sometimes realizing the trend of a coin in a long run period is needed.  ...  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,  ...  In this paper, Long-Short Term Memory (LSTM) networks were connected to a vast scale monetary market forecast mission on the S&P 500, from December 1992 until October 2015.  ... 
doi:10.24200/sci.2020.55034.4040 fatcat:u2lyh5iqlfcb3dwrzt2lglcpbe

The shocklet transform: A decomposition method for the identification of local, mechanism-driven dynamics in sociotechnical time series [article]

David Rushing Dewhurst, Thayer Alshaabi, Dilan Kiley, Michael V. Arnold, Joshua R. Minot, Christopher M. Danforth, Peter Sheridan Dodds
2019 arXiv   pre-print
As an application, we analyze a sociotechnical data source (usage frequencies for a subset of words on Twitter) and highlight our algorithms' utility by using them to extract both a typology of mechanistic  ...  We introduce a qualitative, shape-based, timescale-independent time-domain transform used to extract local dynamics from sociotechnical time series---termed the Discrete Shocklet Transform (DST)---and  ...  for web hosting assistance from Kelly Gothard and useful conversations with Jane Adams and Colin Van Oort.  ... 
arXiv:1906.11710v3 fatcat:2duytavsorfjzbam5ns3bjhkny

Blockchain and Cryptocurrencies [chapter]

Neha Mason, Malka N. Halgamuge, Kamalani Aiyar
2021 Exploring the Convergence of Big Data and the Internet of Things  
that currently account for a substantial proportion of cryptocurrency trading.  ...  These digital currencies, using the emerging blockchain technologies, are forming the basis of the largest unregulated markets in the world.  ...  Conflicts of Interest: The authors declare no conflict of interest. Acknowledgments: We are grateful for the anonymous referees and guest editors for their remarks.  ... 
doi:10.4018/978-1-7998-6650-3.ch007 fatcat:ubsh5ikff5hpzk52l65hai7lgy

A Bayesian Regularized Neural Network for Analyzing Bitcoin Trends

R. Sujatha, V. Mareeswari, Jyotir Moy Chatterjee, Abd Allah A. Mousa, Aboul Ella Hassanien
2021 IEEE Access  
INDEX TERMS Bitcoin, market cap, neural network, realized cap, nonlinear autoregressive with external input (narx), neural network (NN), Levenberg-Marquard (LM), Bayesian Regularization (BR), Scaled Conjugate  ...  The Error histogram and regression plots results indicate that the Bayesian Regularized Neural Network is showing good performance and thus provides a better forecast.  ...  Meanwhile, the ML algorithms have been applied partially to analysis on crypto-currency coins [4] , NN-based methods as Bayesian neural network (BNN) [5] , long-short term memory (LSTM), and recurrent  ... 
doi:10.1109/access.2021.3063243 fatcat:2esozlavpzeg5ounnlqucypbrq

The shocklet transform: a decomposition method for the identification of local, mechanism-driven dynamics in sociotechnical time series

David Rushing Dewhurst, Thayer Alshaabi, Dilan Kiley, Michael V. Arnold, Joshua R. Minot, Christopher M. Danforth, Peter Sheridan Dodds
2020 EPJ Data Science  
As an application, we analyze a sociotechnical data source (usage frequencies for a subset of words on Twitter) and highlight our algorithms' utility by using them to extract both a typology of mechanistic  ...  local dynamics and a data-driven narrative of socially-important events as perceived by English-language Twitter.  ...  for web hosting assistance from Kelly Gothard and useful conversations with Jane Adams and Colin Van Oort.  ... 
doi:10.1140/epjds/s13688-020-0220-x fatcat:q3yb7ptt2nhh7cblf7eyjbet3m

COVID-19 Pandemic: Is the Crypto Market a Safe Haven? The Impact of the First Wave

Darko Vukovic, Moinak Maiti, Zoran Grubisic, Elena M. Grigorieva, Michael Frömmel
2021 Sustainability  
The present study investigated whether the crypto market is a safe haven.  ...  However, the study found spillovers from risky assets (S&P 500) on the crypto market, with Tether as an exception.  ...  The study by [41] applied a wavelet decomposition with a copula approach to analyze the dependence between returns of gold and other assets (bonds, stocks, and exchange rates).  ... 
doi:10.3390/su13158578 fatcat:nkgkczm3tvfzdoluoimxqgyorq

What Drives Bitcoin? An Approach from Continuous Local Transfer Entropy and Deep Learning Classification Models

Andrés García-Medina, Toan Luu Duc Luu Duc Huynh
2021 Entropy  
We test the predictive power of a wide range of determinants on bitcoins' price direction under the continuous transfer entropy approach as a feature selection criterion.  ...  Bitcoin has attracted attention from different market participants due to unpredictable price patterns. Sometimes, the price has exhibited big jumps.  ...  Entropy 2021, 23, 1582 Acknowledgments: We thank Román A. Mendoza for support in the acquisition of the financial time series.  ... 
doi:10.3390/e23121582 pmid:34945888 pmcid:PMC8700167 fatcat:z2vxksscpjgrnpoclemns2gbom

A Comprehensive Statistical Analysis of the Six Major Crypto-Currencies from August 2015 through June 2020

Beatriz Vaz de Melo Mendes, André Fluminense Carneiro
2020 Journal of Risk and Financial Management  
based on the extreme value theory, (3) showed that the returns are weakly autocorrelated and confirmed the presence of long memory as well as short memory in the GARCH volatility, (4) used an econometric  ...  After more than a decade of existence, crypto-currencies may now be considered an important class of assets presenting some unique appealing characteristics but also sharing some features with real financial  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jrfm13090192 fatcat:dd2xeh43wzebdhqtwguyz25whi

Spillover Risks on Cryptocurrency Markets: A Look from VAR-SVAR Granger Causality and Student's-t Copulas

Toan Luu Duc Huynh
2019 Journal of Risk and Financial Management  
This paper contributes a shred of quantitative evidence to the embryonic literature as well as existing empirical evidence regarding spillover risks among cryptocurrency markets.  ...  This result suggests that all coins negatively change in terms of extreme value.  ...  Conflicts of Interest: The author declares no conflict of interest.  ... 
doi:10.3390/jrfm12020052 fatcat:asf4ht2b5ffg5jgszd2o7xyahq

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 [article]

Omer Berat Sezer, Mehmet Ugur Gudelek, Ahmet Murat Ozbayoglu
2019 arXiv   pre-print
as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM).  ...  Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly.  ...  Long Short Term Memory (LSTM) LSTM [58] is a type of RNN where the network can remember both short term and long term values.  ... 
arXiv:1911.13288v1 fatcat:npvyhewuvvcvri4e43jwj3c45y

Deep Learning for Financial Applications : A Survey [article]

Ahmet Murat Ozbayoglu, Mehmet Ugur Gudelek, Omer Berat Sezer
2020 arXiv   pre-print
Lots of different implementations of DL exist today, and the broad interest is continuing.  ...  In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today.  ...  The advantage of LSTM networks lies in the fact that both short term and long term values in the network can be remembered.  ... 
arXiv:2002.05786v1 fatcat:p4ykvxempzajpo66p2z6xaddp4

Persistence in complex systems

S. Salcedo-Sanz, D. Casillas-Pérez, J. Del Ser, C. Casanova-Mateo, L. Cuadra, M. Piles, G. Camps-Valls
2022 Physics reports  
We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence.  ...  The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered.  ...  Acknowledgments This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN).  ... 
doi:10.1016/j.physrep.2022.02.002 fatcat:gkemhragnnaixplufrruyca4ci

Bayesian binary quantile regression for the analysis of Bachelor-to-Master transition

Cristina Mollica, Lea Petrella
2016 Journal of Applied Statistics  
The working group (WG) CMStatistics comprises a number of specialized teams in various research areas of computational and methodological statistics.  ...  The Econometrics and Statistics (EcoSta) and Computational Statistics & Data Analysis (CSDA) are the official journals of the CMStatistics.  ...  the long memory dependence and correlation at short and intermediate lags.  ... 
doi:10.1080/02664763.2016.1263835 fatcat:l5eyielgxrct7hq5ljqeej5ccy
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