Prediction of Cryptocurrency Price Index Using Artificial Neural Networks: A Survey of the Literature

Charandabi, Sina E.; Kamyar, Kamyar

doi:10.24018/ejbmr.2021.6.6.1138

Cited by 30 publications

(18 citation statements)

References 9 publications

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“…A feedforward MLPNN has three layers of input, interiors, and output 44 – 46 . MLPNN benefits from a unique training approach known as the backpropagation, and the utilized activation functions in this method are non-linear 47 .…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

Abdi

Hadipoor

Esmaeili-Faraj

et al. 2022

Sci Rep

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Absorption has always been an attractive process for removing hydrogen sulfide (H2S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H2S capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the H2S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H2S solubility in fifteen various ILs accurately. Six robust machine learning methods, including adaptive neuro-fuzzy inference system, least-squares support vector machine (LS-SVM), radial basis function, cascade, multilayer perceptron, and generalized regression neural networks, are implemented/compared. A vast experimental databank comprising 792 datasets was utilized. Temperature, pressure, acentric factor, critical pressure, and critical temperature of investigated ILs are the affecting parameters of our models. Sensitivity and statistical error analysis were utilized to assess the performance and accuracy of the proposed models. The calculated solubility data and the derived models were validated using seven statistical criteria. The obtained results showed that the LS-SVM accurately predicts H2S solubility in ILs and possesses R2, RMSE, MSE, RRSE, RAE, MAE, and AARD of 0.99798, 0.01079, 0.00012, 6.35%, 4.35%, 0.0060, and 4.03, respectively. It was found that the H2S solubility adversely relates to the temperature and directly depends on the pressure. Furthermore, the combination of OMIM+ and Tf2N-, i.e., [OMIM][Tf2N] ionic liquid, is the best choice for H2S capture among the investigated absorbents. The H2S solubility in this ionic liquid can reach more than 0.8 in terms of mole fraction.

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Section: Theoretical Backgroundmentioning

confidence: 99%

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

Abdi

Hadipoor

Esmaeili-Faraj

et al. 2022

Sci Rep

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“…Stock market has always been a tremendous challenge for analysts due to its uncertain nature. Dase and Pawar (2010), Soni (2011), Budhani et al (2012), Vui et al (2013), Goel et al (2016), Murekachiro et al (2016), and Charandabi and Kamyar (2021) reviewed the implementation of ANN in stock market prediction. These studies found that ANN is the most suited predictor of stock market performance.…”

Section: Applications Of ML In Solving Dynamical Problemsmentioning

confidence: 99%

Machine intelligence in dynamical systems: \A state‐of‐art review

Sahoo

Chakraverty

2022

WIREs Data Min & Knowl

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This article is dedicated to study the impact of machine intelligence (MI) methods viz. various types of Neural models for investigating dynamical systems arising in interdisciplinary areas. Different types of artificial neural network (ANN) methods, viz., recurrent neural network, functional‐link neural network, convolutional neural network, symplectic artificial neural network, genetic algorithm neural network, and so on, are addressed by different researchers to investigate these problems. Although various traditional methods have been developed by researchers to solve these dynamical problems but the existing traditional methods may sometimes be problem dependent, require repetitions of the simulations, and fail to solve nonlinearity behavior. In this regard, neural network model based methods are more general and solutions are continuous over the given domain of integration, self‐adaptive and can be used as a black box. As such, in this article, we have reviewed and analyzed different MI methods, which are applied to investigate these problems. This article is categorized under: Technologies > Computational Intelligence Technologies > Machine Learning Application Areas > Science and Technology

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“…ANN as an alternative to statistical modeling and forecasting has been widely used today because, in some literature, it shows better performance when compared to the regression model [45,47,48]. In addition, ANN is also used to identify, model, and predict complex systems in various cases [49][50][51][52][53], including that was recently used to forecast cryptocurrency volatility [54,55]. To the best of our knowledge, no previous studies have used the ANN approach to model industrial energy demand and its relationship to subsector manufacturing output and climate change in Taiwan.…”

Section: Introductionmentioning

confidence: 99%

Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights

et al. 2022

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The study aims to adopt an artificial neural network (ANN) for modeling industrial energy demand in Taiwan related to the subsector manufacturing output and climate change. This is the first study to use the ANN technique to measure the industrial energy demand–manufacturing output–climate change nexus. The ANN model adopted in this study is a multilayer perceptron (MLP) with a feedforward backpropagation neural network. This study compares the outcomes of three ANN activation functions with multiple linear regression (MLR). According to the estimation results, ANN with a hidden layer and hyperbolic tangent activation function outperforms other techniques and has statistical solid performance values. The estimation results indicate that industrial electricity demand in Taiwan is price inelastic or has a negative value of −0.17 to −0.23, with climate change positively influencing energy demand. The relationship between manufacturing output and energy consumption is relatively diverse at the disaggregated level.

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Prediction of Cryptocurrency Price Index Using Artificial Neural Networks: A Survey of the Literature

Cited by 30 publications

References 9 publications

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

Machine intelligence in dynamical systems: \A state‐of‐art review

Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights

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