Time Series Methodology in STORJ Token Prediction

Chakraborty, Dipnil; Roy, Abhijit Guha

doi:10.1109/icdmw.2019.00042

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…Though some ML models (classification and regression) were applied in predicting price volatility trends of cryptocurrencies, some researchers focused on the comparison of different statistical and ML methods, and also classification and regression‐based ML schemes (Abbatemarco et al, 2018; Bush & Choi, 2019; Chen et al, 2017; Ciaian et al, 2016; Hashish et al, 2019; Mittal et al, 2019; Mohanty et al, 2018; Poongodi et al, 2020; Sovbetov, 2018; Uras et al, 2020; Vaddepalli & Antoney, 2018; Valencia et al, 2019). Furthermore, some schemes integrated various prediction models, including some of the popular classification techniques as well as some popular time‐series forecasting techniques, while considering multiple aspects (Roy et al, 2018; Längkvist et al, 2014; Chakraborty & Roy, 2019; Derbentsev et al, 2019; Wang & Chen, 2020; Poyser, 2019). For example, some researchers studied the results using classical ARIMA models and different ML techniques, such as RF, linear discriminant analysis, logistic regression, and LSTM (Amjad & Shah, 2017; McNally et al, 2018; Saxena, Sukumar, Nadu, & Nadu, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…This includes a collection of techniques, such as SVMs, ANNs, fuzzy logic, genetic algorithms, linear and nonlinear statistical models, DL and RL models, and so on (Atsalakis et al, 2019;Galeshchuk & Mukherjee, 2017;Hitam et al, 2019;Jiang & Liang, 2017;Längkvist et al, 2014;Lahmiri, 2011;Lahmiri & Bekiros, 2019;Nikou et al, 2019;Peng, Albuquerque, de Sá, Padula, & Montenegro, 2018;Radityo et al, 2017;Sarlin & Marghescu, 2011;Sin & Wang, 2017;Tupinambás, Cadence, & Lemos, 2018;Uras et al, 2020). schemes integrated various prediction models, including some of the popular classification techniques as well as some popular time-series forecasting techniques, while considering multiple aspects (Roy et al, 2018;Längkvist et al, 2014;Chakraborty & Roy, 2019;Derbentsev et al, 2019;Wang & Chen, 2020;Poyser, 2019). For example, some researchers studied the results using classical ARIMA models and different ML techniques, such as RF, linear discriminant analysis, logistic regression, and LSTM (Amjad & Shah, 2017;McNally et al, 2018;Saxena, Sukumar, Nadu, & Nadu, 2018).…”

Section: Analysis: Research Contribution Among Different Categoriesmentioning

confidence: 99%

“…The analysis conducted will be further extended by improving the precision of less accurate techniques. STORJ tokens were presented in Chakraborty and Roy (2019) to make transactions in a STORJ network for predicting future price. Different time‐series models were presented, including Box–Jenkins models, NN, and a switching regression model.…”

Section: Techniques For Cryptocurrency Price Predictionmentioning

confidence: 99%

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Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Khedr

Arif

et al. 2021

Intell Sys Acc Fin Mgmt

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Cryptocurrencies are decentralized electronic counterparts of government-issued money. The first and best-known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine-learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.

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Section: Discussionmentioning

confidence: 99%

Section: Analysis: Research Contribution Among Different Categoriesmentioning

confidence: 99%

Section: Techniques For Cryptocurrency Price Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Khedr

Arif

et al. 2021

Intell Sys Acc Fin Mgmt

View full text Add to dashboard Cite

show abstract

“…Using deep learning to detect and classify diseases has gained a lot of attention in the recent years. Researchers, from across the globe, have been using convolutional neural networks (CNNs) for imaging as well as sequential [5] problems. Numerous studies in the last few years have focused on detecting Alzheimer’s disease and other types of dementias.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning-based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease

Chakraborty

Zhuang

Xue

et al. 2022

Preprint

Self Cite

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The prognosis and treatment of the patients suffering from Alzheimer's disease (AD) have been one of the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify genetic variants associated with brain atrophy. Commonly in these analyses, neuroimaging features are extracted based on one of many possible brain atlases with FreeSurf and other popular softwares, which however may lose important information due to our incomplete knowledge about brain function embedded in these suboptimal atlases. To address the issue, we propose convolutional neural network (CNN) models applied to three-dimensional MRI data for the whole brain or multiple divided brain regions to perform completely data-driven and automatic feature extraction. These image-derived features are then used as endophenotypes in Genome-Wide Association Studies (GWAS) to identify associated genetic variants. When applied to the ADNI data, we identified several associated SNPs which have been previously shown to be related to several neurodegenerative/mental disorders such as AD, depression and schizophrenia. Code and supplementary materials are available at https://github.com/Dipnil07. The codes have been implemented using Python, R and Plink softwares.

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“…Most researches in financial time series use linear models like Box-Jenkins [14], different autoregressive conditional heteroskedasticity (ARCH) models [15], the Markov model [16], and many other variations of such models to study and forecast stationary processes and the variance of error values. These models are limited to explain nonlinear behavior of values [17]. In 1990, Tong introduced the threshold autoregressive (TAR) model [18,19], which provides an extensive set of possible dynamics for financial and economic time series compared with different ARCH models.…”

Section: Literature Reviewmentioning

confidence: 99%

Dissecting Tether’s Nonlinear Dynamics during Covid-19

Maiti

Grubišić²,

Vuković

2020

Journal of Open Innovation: Technology, Market, and Complexity

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The present study is on the five cryptocurrency daily mean return time series linearity dynamics during the Covid-19 period. These cryptocurrencies were chosen based on their influence on the market, primarily driven by its market capitalisation. Tether is included as the most important stable coin on the market, nominally pegged to the U.S. dollar (USD). The reason to investigate it is that there are some inconsistencies in its behaviour as opposed to the other four cryptocurrencies. This study found that the behaviour of Tether cryptocurrency daily average return time series pattern is highly nonlinear and chaotic in nature, whereas the other four cryptocurrencies (namely Bitcoin, Ethereum, XRP and Bitcoin Cash) daily average return time series were found to be linear in nature. To further study Tether’s nonlinear time series rich dynamics, this study deployed one category of the regime switching models popularly known as the threshold regressions. The study estimates fairly suggest that both the threshold autoregression (TAR) and smooth transition autoregressive (STAR) models with lag 1 are adequate to capture the rich nonlinear and chaotic dynamics of Tether’s daily average return time series.

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Time Series Methodology in STORJ Token Prediction

Cited by 5 publications

References 13 publications

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Deep Learning-based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease

Dissecting Tether’s Nonlinear Dynamics during Covid-19

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