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標題: | 基於長短期記憶遞迴類神經網路之新台幣兌美元匯率預測模型 A Model Based on LSTM-RNN for Forecasting USD/TWD Exchange Rates |
作者: | Yun-Chung Cheng 鄭允中 |
指導教授: | 呂育道(Yuh-Dauh Lyuu) |
關鍵字: | 匯率預測,遞迴類神經網路,長短期記憶遞迴類神經網路, exchange rate forecasting,recurrent neural network,long short-term memory, |
出版年 : | 2017 |
學位: | 碩士 |
摘要: | 外匯市場是最複雜的金融市場之一。匯率常具有高雜訊、非穩態、非線性等特徵。因此在匯率預測中,使用非線性模型預測較為適當。
本論文建制了一個長短期記憶遞迴類神經網路(long short-term memory recurrent neural network)的預測模型用以預測外匯市場,該方法預測新台幣兌美元之隔日漲跌方向準確度為53.8%。而以 LSTM-RNN (with dropout) 預測各國匯率,得預測準確度最高為韓元兌美元,準確度達55.84%,最低為澳幣兌美元,準確度達49.43%。 Foreign exchange (FX) market is one of the most complex financial systems. Foreign exchange rates typically contain high noise, non-stationarity and non-linearity. As a result, it is necessary to use non-linear models for forcasting purposes. We build a forecasting system based on the LSTM-RNN (long short-term memory recurrent neural network) with dropout model to predict FX markets. This method predicts the direction of change in USD/TWD exchange rates for the next day with 53.8% accuracy. In addition, we use the LSTM-RNN with dropout model to predict the exchange rates of other countries. The best result is the USD/JPY exchange rate, with 55.84% accuracy, and the worst result is the USD/AUD exchange rates, with 49.43% accuracy. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67789 |
DOI: | 10.6342/NTU201701903 |
全文授權: | 有償授權 |
顯示於系所單位: | 資訊工程學系 |
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