Performance evaluation of deep learning and boosted trees for cryptocurrency closing price prediction
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Issue Date
2022-11-09Subjects
artificial intelligencedeep learning
boosted trees
optimisation
forecasting
cryptocurrencies
Subject Categories::G700 Artificial Intelligence
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The emergence of cryptocurrencies has drawn significant investment capital in recent years with an exponential increase in market capitalization and trade volume. However, the cryptocurrency market is highly volatile and burdened with substantial heterogeneous datasets characterized by complex interactions between predictors, which may be difficult for conventional techniques to achieve optimal results. In addition, volatility significantly impacts investment decisions; thus, investors are confronted with how to determine the price and assess their financial investment risks reasonably. This study investigates the performance evaluation of a genetic algorithm tuned Deep Learning (DL) and boosted tree-based techniques to predict several cryptocurrencies' closing prices. The DL models include Convolutional Neural Networks (CNN), Deep Forward Neural Networks, and Gated Recurrent Units. The study assesses the performance of the DL models with boosted tree-based models on six cryptocurrency datasets from multiple data sources using relevant performance metrics. The results reveal that the CNN model has the least mean average percentage error of 0.08 and produces a consistent and highest explained variance score of 0.96 (on average) compared to other models. Hence, CNN is more reliable with limited training data and easily generalizable for predicting several cryptocurrencies' daily closing prices. Also, the results will help practitioners obtain a better understanding of crypto market challenges and offer practical strategies to lower risks.Citation
Oyedele AA, Ajayi AO, Oyedele LO, Bello SO, Jimoh KO (2023) 'Performance evaluation of deep learning and boosted trees for cryptocurrency closing price prediction', Expert Systems with Applications, 213 (C), 119233Publisher
ElsevierJournal
Expert Systems with ApplicationsType
ArticleLanguage
enISSN
0957-4174Sponsors
Not Applicableae974a485f413a2113503eed53cd6c53
10.1016/j.eswa.2022.119233
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