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• W4285105969 abstract "Cryptocurrency has recently attracted substantial interest from investors due to its underlying philosophy of decentralization and transparency. Considering cryptocurrency&#x2019;s volatility and unique characteristics, accurate price prediction is essential for developing successful investment strategies. To this end, the authors of this work propose a novel framework that predicts the price of Bitcoin (BTC), a dominant cryptocurrency. For stable prediction performance in unseen price range, the change point detection technique is employed. In particular, it is used to segment time-series data so that normalization can be separately conducted based on segmentation. In addition, on-chain data, the unique records listed on the blockchain that are inherent in cryptocurrencies, are collected and utilized as input variables to predict prices. Furthermore, this work proposes self-attention-based multiple long short-term memory (SAM-LSTM), which consists of multiple LSTM modules for on-chain variable groups and the attention mechanism, for the prediction model. Experiments with real-world BTC price data and various method setups have proven the proposed framework&#x2019;s effectiveness in BTC price prediction. The results are promising, with the highest MAE, RMSE, MSE, and MAPE values of 0.3462, 0.5035, 0.2536, and 1.3251, respectively." @default.
• W4285105969 created "2022-07-14" @default.
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• W4285105969 date "2022-01-01" @default.
• W4285105969 modified "2023-09-30" @default.
• W4285105969 title "A Deep Learning-Based Cryptocurrency Price Prediction Model That Uses On-Chain Data" @default.
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• W4285105969 doi "https://doi.org/10.1109/access.2022.3177888" @default.
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