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- W4214819857 abstract "The proliferation of various virtual currencies represented by Bitcoin and Ether and the continuous price increase have attracted a great number of investors to make investments. All circles of society also gradually attach importance to the research of virtual currency. It is also a hot research topic to predict the future price of virtual currency by obtaining its historical price. In the experiment, we noticed that some commonly used machine learning algorithms showed great deviations in predicting the price of virtual currency. In order to achieve higher accuracy and effectiveness in predicting the price of virtual currency, this paper proposes a segmented integrated learning (ensemble-SVR) method based on SVR algorithm. Simulation results show that compared with SVR and other mainstream machine learning algorithms, our algorithm has significant advantages in predicting virtual currency prices." @default.
- W4214819857 created "2022-03-05" @default.
- W4214819857 creator A5010810562 @default.
- W4214819857 date "2022-01-21" @default.
- W4214819857 modified "2023-09-27" @default.
- W4214819857 title "Virtual currency price prediction based on segmented integrated learning" @default.
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- W4214819857 doi "https://doi.org/10.1109/icpeca53709.2022.9719070" @default.
- W4214819857 hasPublicationYear "2022" @default.
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