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

鄭允中(Yun-Chung Cheng)

指導教授 ： 呂育道

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2017年)

https://doi.org/10.6342/NTU201701903

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%。

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

並列摘要

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.

並列關鍵字

exchange rate forecasting ； recurrent neural network ； long short-term memory

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