Abstract:

After the invention of Bitcoin as well as other blockchain-based peer-to-peer payment systems, the cryptocurrency market has rapidly gained popularity. Consequently, the volatility of the various cryptocurrency prices attracts substantial attention from both investors and researchers. It is a challenging task to forecast the prices of cryptocurrencies due to the non-stationary prices and the stochastic effects in the market. Current cryptocurrency price forecasting models mainly focus on analyzing exogenous factors, such as macro-financial indicators, blockchain information, and social media data – with the aim of improving the prediction accuracy. However, the intrinsic systemic noise, caused by market and political conditions, is complex to interpret. Inspired by the strong correlations among cryptocurrencies and the powerful modelling capability displayed by deep learning techniques, we propose a Weighted & Attentive Memory Channels model to predict the daily close price and the fluctuation of cryptocurrencies. In particular, our proposed model consists of three modules: an Attentive Memory module combines a Gated Recurrent Unit with a self-attention component to establish attentive memory for each input sequence; a Channel-wise Weighting module receives the price of several heavyweight cryptocurrencies and learns their interdependencies by recalibrating the weights for each sequence; and a Convolution & Pooling module extracts local temporal features, thereby improving the generalization ability of the overall model. In order to validate the proposed model, we conduct a battery of experiments. The results show that our proposed scheme achieves state-of-the-art performance and outperforms the baseline models in prediction error, accuracy, and profitability.

Bibtex

@article{ZHANG2021115378,
      title = {Forecasting cryptocurrency price using convolutional neural networks with weighted and attentive memory channels},
      journal = {Expert Systems with Applications},
      volume = {183},
      pages = {115378},
      year = {2021},
      issn = {0957-4174},
      doi = {https://doi.org/10.1016/j.eswa.2021.115378},
      url = {https://www.sciencedirect.com/science/article/pii/S0957417421008046},
      author = {Zhuorui Zhang and Hong-Ning Dai and Junhao Zhou and Subrota Kumar Mondal and Miguel Martínez García and Hao Wang},
}