【Author】
Kim, Gyeongho; Shin, Dong-Hyun; Choi, Jae Gyeong; Lim, Sunghoon
【Abstract】Cryptocurrency has recently attracted substantial interest from investors due to its underlying philosophy of decentralization and transparency. Considering cryptocurrency'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'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.
【Keywords】Cryptocurrency; Blockchains; Predictive models; Investment; Bitcoin; Gold; Data models; Blockchain; cryptocurrency; Bitcoin; deep learning; prediction methods; change detection algorithms
【标题】基于深度学习的链上数据分析加密货币价格预测模型
【摘要】由于其去中心化和透明的基本理念，加密货币最近引起了投资者的极大兴趣。考虑到加密货币的市场波动性和独特性，准确的价格预测对于制定成功的投资策略至关重要。为此，本文的作者提出了一个新颖的框架来预测比特币（BTC）的价格，这是一种主流的加密货币。为了在难以预料的价格范围内保持稳定的预测性能，采用了变化点检测技术。特别是对时间序列数据进行切分，以便在切分的基础上单独进行归一化。此外，链上数据，即加密货币固有的区块链上列出的唯一记录，被收集并用作预测价格的输入变量。此外，这项工作提出了基于自专注的多重长短期记忆（SAM-LSTM），它由多个用于链上变量组的 LSTM 模块和专注机制组成，用于预测模型。真实世界的 BTC 价格数据和各种方法设置的实验证明了所提出的框架在 BTC 价格预测中的有效性。结果是鼓舞人心的，最高 MAE、RMSE、MSE 和 MAPE 值分别为 0.3462、0.5035、0.2536 和 1.3251。
【关键词】加密货币；区块链；预测模型；投资；比特币；金子；数据模型；区块链；比特币；深度学习；预测方法；变化检测算法
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