Cryptocurrency price forecasting – A comparative analysis of ensemble learning and deep learning methods

Bouteska, Ahmed; Abedin, Mohammad Zoynul; Hajek, Petr; Yuan, Kunpeng

doi:10.1016/j.irfa.2023.103055

Cited by 6 publications

(3 citation statements)

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“…The dataset, in turn, is used as the groundwork for training and testing machine learning models to forecast future price movements [14]. The selection of input features is key in the model prediction as the factors such as trading volume, sentiment on social media and blockchain network metrics play a major role in driving the cryptocurrency prices [4]. In this study, machine learning algorithms include widely used techniques, for instance, linear regression, support vector machines, random forest, and deep learning networks [1].…”

Section: Introductionmentioning

confidence: 99%

“…nilofar.mulla2006@gmail.com, 0000-0002-9255-0311 3 PhD Scholar, Department of Mathematics, Bhattadev University, Assam, India. kswapnil9234@gmail.com 0009-0007-1445-9364 4 Assistant Professor, Lovely Professional University. Dhami.navjot@gmail.com 0000-0002-7523-2282 5 Associate Professor, Lovely Professional University, someetsingh84@gmail.com 0000-0002-9816-6521 engineering techniques are used to extract meaning from the raw data and improve the model's performance with respect to the prediction [8].…”

Section: Introductionmentioning

confidence: 99%

“…This scientific report will provide a complete output and discussion of the outcome of machine learning models in predicting the prices of cryptocurrencies [1]. The experiments illustrate that different algorithms have shown different levels of precision and reliability [4]. While some models show a decent short-term performance in price prediction, others find it challenging to address the complicated and nonlinear nature of cryptocurrency markets [7].…”

Section: Introductionmentioning

confidence: 99%

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Performance Scrutiny of Price Prediction on Blockchain Technology Using Machine Learning

Jayanta Aich

2024

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Objective: This study, aiming to assess the accurateness of machine learning algorithms for the prediction of cryptocurrency prices, using blockchain technology, was conducted to attest to its effectiveness. The main goal is to evaluate the precision, solidity, and prospects of such models under variable and fragmented trading premises. Methodology: Dataset containing historical crypto prices, transaction volumes, emotions of investors, etc. is formed from different and various sources. Different machine learning approaches including linear regression, support vector machine, random forest, and deep learning networks will be used to construct the prediction model. The split of the dataset into training and testing subsets is performed to better evaluate and estimate the models' performance. Feature engineering techniques and parameter tuning are included in the process of making models more precise. Models are the ones which are the trained using historical data and then later tested out on the unseen data for the purpose of accurate results and generalization power. Results and Discussion: From the experiment it is evident that a shift of different machine learning algorithms exhibits differing outcomes. While certain models can yield admirable results in forecasting crypto prices in shorter terms, however, there are some models that are not capable of capturing the complicated impurities. Factors like data quality, function selection and model complexity are at the core of predictive power. The learning networks demonstrated the capability to reproduce nonlinearity and temporal relatedness in cryptocurrency value data. Conclusion: Cryptocurrency forecasting through blockchain technology involves the use of machine learning techniques which has a lot of potential. The success of these models also depends on the quality of the data, feature refinement and model choice. Research progress should focus on refining currently existing models, considering new data inputs, and introduction of advanced machine learning methods which should be able to increase forecast accuracy in rapidly changing cryptocurrency markets.

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Section: Introductionmentioning

confidence: 99%