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- W4384346286 abstract "Cryptocurrencies have been experiencing explosive growth in recent years, which attracts not only investors or traders but also researchers. Many models have been proposed for forecasting or predicting cryptocurrencies. In this paper, we propose a labeled sequence data model that preserves sequential pattern information and a Seq2class model to categorize such patterns. To reinforce deep learning with the Seq2class model, we propose a mathematically viable method to expand our training dataset. Initial results from implementing our proposed methods exhibit some promising prospects. If the methods were implemented as a decision support system, investment transactions could be profitable." @default.
- W4384346286 created "2023-07-15" @default.
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- W4384346286 date "2023-01-01" @default.
- W4384346286 modified "2023-10-14" @default.
- W4384346286 title "Return Forecasting for Cryptocurrency Using Labeled Sequences and Reinforced Seq2Class Model" @default.
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- W4384346286 doi "https://doi.org/10.1007/978-3-031-36822-6_21" @default.
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