A novel hybrid walk-forward ensemble optimization for time series cryptocurrency prediction
Cryptocurrency is an advanced digital currency that is secured by encryption, making it nearly impossible to forge or duplicate. Many cryptocurrencies are blockchain-based with decentralized networks. The prediction of cryptocurrency prices is a very difficult task because of the absence of an appro...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844022031504 |
| _version_ | 1811320609158725632 |
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| author | David Opeoluwa Oyewola Emmanuel Gbenga Dada Juliana Ngozi Ndunagu |
| author_facet | David Opeoluwa Oyewola Emmanuel Gbenga Dada Juliana Ngozi Ndunagu |
| author_sort | David Opeoluwa Oyewola |
| collection | DOAJ |
| description | Cryptocurrency is an advanced digital currency that is secured by encryption, making it nearly impossible to forge or duplicate. Many cryptocurrencies are blockchain-based with decentralized networks. The prediction of cryptocurrency prices is a very difficult task because of the absence of an appropriate analytical basis to substantiate their claims. Cryptocurrencies are also dependent on several variables, such as technical advancement, internal competition, market pressure, economic concerns, security, and political considerations. This paper proposed the hybrid walk-forward ensemble optimization technique and applied it to predict the daily prices of fifteen cryptocurrencies, such as Cardano (ADA-USD), Bitcoin (BTC-USD), Dogecoin (DOGE-USD), Ethereum Classic (ETC-USD), Chainlink (LINK-USD), Litecoin (LTC-USD), NEO (NEO-USD), Tron (TRX-USD), Tether (USDT-USD), NEM (XEM-USD), Stellar (XLM-USD), Ripple (XRP-USD), and Tezos (XTZ-USD). A performance comparison of these cryptocurrencies was done using classical statistical models, machine learning algorithms, and deep learning algorithms on different cryptocurrency time series. Simulation results show that our proposed model performed better in terms of cryptocurrency prediction accuracy compared to the classical statistical model and machine and deep learning algorithms used in this paper. |
| first_indexed | 2024-04-13T13:01:03Z |
| format | Article |
| id | doaj.art-b9a34988e5bf4d958b3059c2ffacd31d |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-13T13:01:03Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-b9a34988e5bf4d958b3059c2ffacd31d2022-12-22T02:45:54ZengElsevierHeliyon2405-84402022-11-01811e11862A novel hybrid walk-forward ensemble optimization for time series cryptocurrency predictionDavid Opeoluwa Oyewola0Emmanuel Gbenga Dada1Juliana Ngozi Ndunagu2Department of Mathematics and Statistics, Federal Univerisity Kashere, Gombe, Nigeria; Corresponding author.Department of Mathematical Sciences, Faculty of Science, University of Maiduguri, NigeriaDepartment of Computer Science, National Open University of Nigeria, NigeriaCryptocurrency is an advanced digital currency that is secured by encryption, making it nearly impossible to forge or duplicate. Many cryptocurrencies are blockchain-based with decentralized networks. The prediction of cryptocurrency prices is a very difficult task because of the absence of an appropriate analytical basis to substantiate their claims. Cryptocurrencies are also dependent on several variables, such as technical advancement, internal competition, market pressure, economic concerns, security, and political considerations. This paper proposed the hybrid walk-forward ensemble optimization technique and applied it to predict the daily prices of fifteen cryptocurrencies, such as Cardano (ADA-USD), Bitcoin (BTC-USD), Dogecoin (DOGE-USD), Ethereum Classic (ETC-USD), Chainlink (LINK-USD), Litecoin (LTC-USD), NEO (NEO-USD), Tron (TRX-USD), Tether (USDT-USD), NEM (XEM-USD), Stellar (XLM-USD), Ripple (XRP-USD), and Tezos (XTZ-USD). A performance comparison of these cryptocurrencies was done using classical statistical models, machine learning algorithms, and deep learning algorithms on different cryptocurrency time series. Simulation results show that our proposed model performed better in terms of cryptocurrency prediction accuracy compared to the classical statistical model and machine and deep learning algorithms used in this paper.http://www.sciencedirect.com/science/article/pii/S2405844022031504CryptocurrencyBlockChainGated recurrent unitWalk-forwardOptimization |
| spellingShingle | David Opeoluwa Oyewola Emmanuel Gbenga Dada Juliana Ngozi Ndunagu A novel hybrid walk-forward ensemble optimization for time series cryptocurrency prediction Heliyon Cryptocurrency BlockChain Gated recurrent unit Walk-forward Optimization |
| title | A novel hybrid walk-forward ensemble optimization for time series cryptocurrency prediction |
| title_full | A novel hybrid walk-forward ensemble optimization for time series cryptocurrency prediction |
| title_fullStr | A novel hybrid walk-forward ensemble optimization for time series cryptocurrency prediction |
| title_full_unstemmed | A novel hybrid walk-forward ensemble optimization for time series cryptocurrency prediction |
| title_short | A novel hybrid walk-forward ensemble optimization for time series cryptocurrency prediction |
| title_sort | novel hybrid walk forward ensemble optimization for time series cryptocurrency prediction |
| topic | Cryptocurrency BlockChain Gated recurrent unit Walk-forward Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2405844022031504 |
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