A Deep Learning-Based Cryptocurrency Price Prediction Model That Uses On-Chain Data

【Author】 Kim, Gyeongho; Shin, Dong-Hyun; Choi, Jae Gyeong; Lim, Sunghoon

【Source】IEEE ACCESS

【影响因子】3.476

【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

【发表时间】2022

【收录时间】2022-06-15

【文献类型】理论性文章

【主题类别】

区块链治理-市场治理-数字货币