Wavelet Denoising and Attention-based RNN-ARIMA Model to Predict Forex
Price
- URL: http://arxiv.org/abs/2008.06841v1
- Date: Sun, 16 Aug 2020 05:32:40 GMT
- Title: Wavelet Denoising and Attention-based RNN-ARIMA Model to Predict Forex
Price
- Authors: Zhiwen Zeng and Matloob Khushi
- Abstract summary: A novel approach that integrates the wavelet denoising, Attention-based Recurrent Neural Network (ARNN), and Autoregressive Integrated Moving Average (ARIMA) is proposed.
Our experiments on USD/JPY five-minute data outperforms the baseline methods.
- Score: 0.30458514384586405
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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%.
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