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

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.

Analysis of the Artificial Neural Network Approach in the Extreme Learning Machine Method for Mining Sales Forecasting Development

“…The study made the model more productive in obtaining accurate results for each item in the product life cycle. Meanwhile, [9] applied ELM integrated with LSTM to Bitcoin price forecasting. Their study matches the different machine learning algorithms to corresponding multiscale components and constructs the ensemble prediction models based on machine learning and multiscale analysis.…”

“…Their study matches the different machine learning algorithms to corresponding multiscale components and constructs the ensemble prediction models based on machine learning and multiscale analysis. The results showed that the ensemble models can achieve a prediction accuracy of 95.12% with enhanced performance than the benchmark models [9]. Moreover, Cholid and Aly [10] forecasted spiral and leaf spring products for fourwheeled vehicles using the Artificial Neural Network Backpropagation method.…”