论文标题
小波denoising和基于注意力的RNN-ARIMA模型可预测外汇价格
Wavelet Denoising and Attention-based RNN-ARIMA Model to Predict Forex Price
论文作者
论文摘要
外汇市场的每一次趋势变化都带来了一个巨大的机会以及投资者的风险。在任何有效的对冲或投机策略中，对外汇价格的准确预测是至关重要的要素。但是，外汇市场的复杂性使预测的问题具有挑战性，这引发了各种学科的广泛研究。在本文中，提出了一种整合小波denoising，基于注意力的复发性神经网络（ARNN）的新方法，并提出了自回归的综合运动平均值（ARIMA）。小波变换消除了时间序列的噪声以稳定数据结构。 ARNN模型捕获了序列中的鲁棒和非线性关系，而Arima可以很好地符合顺序信息的线性相关性。通过三个模型的杂交，该方法能够对动态系统（例如外汇市场）进行建模。我们对美元/JPY五分钟数据的实验优于基线方法。发现混合方法的根平方 - 纠错（RMSE）为1.65，定向精度约为76％。
Every change of trend in the forex market presents a great opportunity as well as a risk for investors. Accurate forecasting of forex prices is a crucial element in any effective hedging or speculation strategy. However, the complex nature of the forex market makes the predicting problem challenging, which has prompted extensive research from various academic disciplines. In this paper, a novel approach that integrates the wavelet denoising, Attention-based Recurrent Neural Network (ARNN), and Autoregressive Integrated Moving Average (ARIMA) are proposed. Wavelet transform removes the noise from the time series to stabilize the data structure. ARNN model captures the robust and non-linear relationships in the sequence and ARIMA can well fit the linear correlation of the sequential information. By hybridization of the three models, the methodology is capable of modelling dynamic systems such as the forex market. Our experiments on USD/JPY five-minute data outperforms the baseline methods. Root-Mean-Squared-Error (RMSE) of the hybrid approach was found to be 1.65 with a directional accuracy of ~76%.
