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

. In recent years, digital currencies have fourished on a considerable scale, and the markets of digital currencies have generated a nonnegligible impact on the whole fnancial system. Under this background, the accurate prediction of cryptocurrency prices could be a prerequisite for managing the risk of both cryptocurrency markets and fnancial systems. Considering the multiscale attributes of cryptocurrency price, we match the diferent machine learning algorithms to corresponding multiscale components and construct the ensemble prediction models based on machine learning and multiscale analysis. Te 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. Te 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 diferent algorithms are applicable for components with varying time scales.