Bitcoin Price Forecasting: An Integrated Approach Using Hybrid LSTM-ELM Models

【Author】 Luo, Changqing; Pan, Lurun; Chen, Binwei; Xu, Huiru

【Source】MATHEMATICAL PROBLEMS IN ENGINEERING

【影响因子】1.430

【Abstract】In recent years, digital currencies have flourished on a considerable scale, and the markets of digital currencies have generated a nonnegligible impact on the whole financial system. Under this background, the accurate prediction of cryptocurrency prices could be a prerequisite for managing the risk of both cryptocurrency markets and financial systems. Considering the multiscale attributes of cryptocurrency price, we match the different machine learning algorithms to corresponding multiscale components and construct the ensemble prediction models based on machine learning and multiscale analysis. The Bitcoin price series, respectively, from 2017/11/24 to 2020/4/21 and 2020/4/22 to 2020/11/27, is selected as the training and prediction datasets. The empirical results show that the ensemble models can achieve a prediction accuracy of 95.12%, with better performance than the benchmark models, and the proposed models are robust in upward and downward market conditions. Meanwhile, the different algorithms are applicable for components with varying time scales.

【Keywords】

【发表时间】2022 9-Nov

【收录时间】2022-12-08

【文献类型】理论模型

【主题类别】

区块链治理-市场治理-价格预测