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|Authors: ||林泰宏;Lin, Tai-Hung|
|Keywords: ||深度學習;遞迴神經網絡;門控遞迴單元;黃金價格預測;Deep learning;recurrent neural network;gated recurrent unit;gold price prediction|
|Issue Date: ||2020-09-02 17:56:55 (UTC+8)|
;According to the application of deep learning to the prediction of business field and time series, this research proposes a gated recurrent unit of dual GRU model as a gold price prediction. This method includes data collection and pre-processing, design and training of GRU models, and testing. And evaluation.
The price prediction of gold is affected by many factors including oil, the US dollar, inflation, and economic policies and contingency events. It is quite difficult to accurately predict such a non-linear and unstable time series, and add them to the prediction model as much as possible for the accuracy of the prediction. Therefore, this paper also incorporates the representative indexes among them, oil price and VIX index to add to the model adjustment. Using the gated recurrent unit GRU solves the problem that the traditional recurrent neural network cannot solve the long-term dependence. This article attempts to use them to build a model to predict the price trend of gold.
In this paper, we design multiple GRU models based on recurrent neural network. After sufficient training, adjustment and optimization, we use VIX and crude oil price-related data as data characteristics during training to predict the closing price of gold the next day. The 3 dimensions of dual GRU model has got better prediction results, which proves the performance of the dual GRU recurrent neural network model in the prediction of financial market. It has certain innovation, and the model can provide reference for time series related research and has certain value.
|Appears in Collections:||[資訊管理學系碩士在職專班 ] 博碩士論文|
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