Initial Coin Offering Prediction Comparison Using Ridge Regression, Artificial Neural Network, Random Forest Regression, and Hybrid ANN-Ridge

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dc.contributor.authorTran, Toai Kim
dc.contributor.authorSenkerik, Roman
dc.contributor.authorVo, Hahn Thi Xuan
dc.contributor.authorVo, Huan Minh
dc.contributor.authorUlrich, Adam
dc.contributor.authorMusil, Marek
dc.contributor.authorZelinka, Ivan
dc.coverage.issue2cs
dc.coverage.volume29cs
dc.date.accessioned2024-01-11T09:48:07Z
dc.date.available2024-01-11T09:48:07Z
dc.date.issued2023-12-31cs
dc.description.abstractCan machine learning take a prediction to win an investment in ICO (Initial Coin Offering)? In this research work, our objective is to answer this question. Four popular and lower computational demanding approaches including Ridge regression (RR), Artificial neural network (ANN), Random forest regression (RFR), and a hybrid ANN-Ridge regression are compared in terms of accuracy metrics to predict ICO value after six months. We use a dataset collected from 109 ICOs that were obtained from the cryptocurrency websites after data preprocessing. The dataset consists of 12 fields covering the main factors that affect the value of an ICO. One-hot encoding technique is applied to convert the alphanumeric form into a binary format to perform better predictions; thus, the dataset has been expanded to 128 columns and 109 rows. Input data (variables) and ICO value are non-linear dependent. The Artificial neural network algorithm offers a bio-inspired mathematical model to solve the complex non-linear relationship between input variables and ICO value. The linear regression model has problems with overfitting and multicollinearity that make the ICO prediction inaccurate. On the contrary, the Ridge regression algorithm overcomes the correlation problem that independent variables are highly correlated to the output value when dealing with ICO data. Random forest regression does avoid overfitting by growing a large decision tree to minimize the prediction error. Hybrid ANN-Ridge regression leverages the strengths of both algorithms to improve prediction accuracy. By combining ANN’s ability to capture complex non-linear relationships with the regularization capabilities of Ridge regression, the hybrid can potentially provide better predictive performance compared to using either algorithm individually. After the training process with the cross-validation technique and the parameter fitting process, we obtained several models but selected three of the best in each algorithm based on metrics of RMSE (Root Mean Square Error), R2 (R-squared), and MAE (Mean Absolute Error). The validation results show that the presented Ridge regression approach has an accuracy of at most 99% of the actual value. The Artificial neural network predicts the ICO value with an accuracy of up to 98% of the actual value after six months. Additionally, the Random forest regression and the hybrid ANN-Ridge regression improve the predictive accuracy to 98% actual value.en
dc.formattextcs
dc.format.extent283-294cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2023 vol. 29, ÄŤ. 2, s. 283-294. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2023.2.283en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttps://hdl.handle.net/11012/244255
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/282cs
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0en
dc.subjectredictionen
dc.subjectICOen
dc.subjectMulti-correlationen
dc.subjectRidge regressionen
dc.subjectLinear regressionen
dc.subjectNeural networksen
dc.subjectRandom foresten
dc.subjectOne-hot encodingen
dc.titleInitial Coin Offering Prediction Comparison Using Ridge Regression, Artificial Neural Network, Random Forest Regression, and Hybrid ANN-Ridgeen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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Vol. 29, No. 2