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用于比特币价格预测的多尺度残差 CNN 与 LSTM

文章 arXiv papers · 作者: Qiutong Guo et al.
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总结

论文提出 MRC-LSTM，该模型结合多尺度残差卷积模块和长短期记忆网络，用于预测比特币每日 USD 收盘价。一维卷积用于检测并融合多变量序列中不同时间尺度的特征，而 LSTM 用于学习较长期的依赖关系。输入包括比特币交易信息，以及宏观经济变量和投资者关注度等外部因素。

实验将该模型与其他网络结构进行比较，并报告称其在比特币预测任务上的表现显著更好。研究还在以太坊和莱特币上开展了额外实验，作为该模型可用于短期多变量加密货币预测的支持。所提供的介绍没有给出数据时段、评估指标、详细基准模型或样本外交易结果，因此无法说明预测准确度的提升是否能转化为盈利策略，或能否在其他情境中持续。

核心观点

  • MRC-LSTM 将多尺度残差卷积网络与 LSTM 结合，用于预测比特币收盘价。
  • 卷积部分检测并融合多变量数据中的跨时间尺度特征。
  • LSTM 部分用于学习序列中的较长期依赖关系。
  • 研究除了使用交易数据，还纳入宏观经济变量和投资者关注度指标。
  • 报告的比较结果更支持所提出的模型，并包含其他加密货币实验，但介绍中没有交易表现证据。

标签

加密资产机器学习统计学波动率
全文
# MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price


# MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price









Bitcoin, one of the major cryptocurrencies, presents great opportunities and challenges with its tremendous potential returns accompanying high risks. The high volatility of Bitcoin and the complex factors affecting them make the study of effective price forecasting methods of great practical importance to financial investors and researchers worldwide. In this paper, we propose a novel approach called MRC-LSTM, which combines a Multi-scale Residual Convolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to implement Bitcoin closing price prediction. Specifically, the Multi-scale residual module is based on one-dimensional convolution, which is not only capable of adaptive detecting features of different time scales in multivariate time series, but also enables the fusion of these features. LSTM has the ability to learn long-term dependencies in series, which is widely used in financial time series forecasting. By mixing these two methods, the model is able to obtain highly expressive features and efficiently learn trends and interactions of multivariate time series. In the study, the impact of external factors such as macroeconomic variables and investor attention on the Bitcoin price is considered in addition to the trading information of the Bitcoin market. We performed experiments to predict the daily closing price of Bitcoin (USD), and the experimental results show that MRC-LSTM significantly outperforms a variety of other network structures. Furthermore, we conduct additional experiments on two other cryptocurrencies, Ethereum and Litecoin, to further confirm the effectiveness of the MRC-LSTM in short-term forecasting for multivariate time series of cryptocurrencies.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。