A New Forecasting Framework for Bitcoin Price with LSTM
A New Forecasting Framework for Bitcoin Price with LSTM
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使用 LSTM 的比特币价格新预测框架
DOI：
10.1109/icdmw.2018.00032
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Ruei
中科院分区：
文献类型：
--
作者：
Chih;Chih;Yu;Ruei
长短期记忆（LSTM）网络是深度学习中最先进的序列学习，用于时间序列预测。然而，应用于金融时间序列预测的研究较少，特别是在加密货币预测方面。因此，我们提出了一个新的LSTM模型预测框架，用两种不同的LSTM模型（传统的LSTM模型和带有AR（2）模型的LSTM）来预测比特币的每日价格。使用2018年1月1日至2018年7月28日期间的每日比特币价格数据评估了所提出模型的性能，共208条记录。结果表明，AR（2）模型具有良好的预测精度。测试均方误差（MSE），均方根误差（RMSE），平均绝对百分比误差（MAPE）和平均绝对误差（MAE）分别用于比特币价格预测。我们提出的具有AR（2）模型的LSTM优于传统的LSTM模型。本研究的贡献在于为比特币价格预测提供了一个新的预测框架，可以克服和改善LSTM中输入变量选择的问题，而无需严格的数据假设。结果显示，它可能适用于各种加密货币预测，行业实例，如医疗数据或金融时间序列数据。
Long short-term memory (LSTM) networks are a state-of-the-art sequence learning in deep learning for time series forecasting. However, less study applied to financial time series forecasting especially in cryptocurrency prediction. Therefore, we propose a new forecasting framework with LSTM model to forecasting bitcoin daily price with two various LSTM models (conventional LSTM model and LSTM with AR(2) model). The performance of the proposed models are evaluated using daily bitcoin price data during 2018/1/1 to 2018/7/28 in total 208 records. The results confirmed the excellent forecasting accuracy of the proposed model with AR(2). The test mean squared error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) for bitcoin price prediction, respectively. The our proposed LSTM with AR(2) model outperformed than conventional LSTM model. The contribution of this study is providing a new forecasting framework for bitcoin price prediction can overcome and improve the problem of input variables selection in LSTM without strict assumptions of data assumption. The results revealed its possible applicability in various cryptocurrencies prediction, industry instances such as medical data or financial time-series data.