Abstract
As the leading component of the financial market, the price formation mechanism of gold futures has been attracting extra attention of financiers and scholars. However, the data of gold futures price belongs to time series, and its forecast is very challenging owing to its chaotic, noisy, and non-stationary characteristics in data. A new forecast model named SGRU-AM, based on the special gated recurrent unit (SGRU) and attention mechanism (AM) is proposed in this paper to tackle these challenges. SGRU is the rectified model of gated recurrent unit (GRU) though executing the 1-tanh function on the reset gate output of the basic GRU to transform the value range of the reset gate value and adjusting the memory ratio between the current moment and the previous moment. Firstly, SGRU has the advantage of capturing long-distance information and can forecast the closing price of gold futures in the next trading day. Then, AM is introduced to adjust the SGRU time dimension’s feature expression, so that the model can obtain more comprehensive feature information, learn the importance of current local sequence features and improve the forecast accuracy. Taking China’s gold futures as an example, the gold futures data of the Shanghai Futures Exchange are selected from January 9, 2008, to May 31, 2021. Compared with the baseline methods, the experimental results show that SGRU-AM has the best performance among all baseline models in forecast efficiency and forecast accuracy.
Highlights
In recent years, the world economic market has been turbulent and changeable
The first classical time series methods used in the field of financial time series analysis include autoregressive moving average model (ARMA) [4], [5], [6], generalized autoregressive conditional heteroskedasticity (GARCH) [7], etc
In addition to the classical econometric time series forecasting methods, decision tree [8], [9], [10], genetic algorithm [11], support vector machine (SVM) [12] and other machine learning methods have been implemented to financial price forecasting [13]
Summary
JINGYANG WANG2, YIFAN LI2, TINGTING WANG2, JIAZHENG LI2, HAIYAO WANG3, AND PENGFEI LIU1*.
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