DOI: https://doi.org/10.21203/rs.3.rs-1136379/v1
Due to the non-linear and complex characteristics of oil futures prices, they are easily affected by external factors. Therefore, predicting the price of oil futures is a very challenging topic in deep learning time series, but the existing literature lacks research on introducing relevant features. Therefore, the purpose of this paper is to use a two-way attention mechanism to predict oil futures prices and import gold prices as features.
The data source is taken from Yahoo Finance, providing 5146 items from 2000/02/28 to 2020/06/29. The paper uses CNNBiLSTM, CNNBiGRU models and Attention CNNBiLSTM, Attention CNNBiGRU, Dual Attention CNNBiGRU, Dual Attention CNNBiLSTM and TPA-LSTM, Prophet, ARIMA that increase the attention mechanism to conduct experiments to find the best model. Moreover, explore the sliding window review days to deal with two-dimensional time series data and the influence of the delay of the typical days to predict the experiment's impact.
The experimental results show that increasing the number of review days will decrease the prediction accuracy. The new model is imported using the two-dimensional time series method, and the attention mechanism is integrated. Importing CNNBiGRU with a dual attention mechanism from 80% of the training items and 20% of the test items in the experimental data set can improve the overall prediction accuracy. For example, the model BiGRU has improved the RMSE from 2.79 to 1.85 by adding a dual attention mechanism. In addition, it is found that if the regression model has large fluctuations in the prediction data, it will increase the RMSE and MAE, especially in the experimental items of 96% training and 4% testing data sets.