||Exchange rate prediction is a typical time series prediction problem which has been modeled by Autoregressive integrated moving average (ARIMA), Seasonal ARIMA as well as Artificial neural networks (ANN) such as Long Short-Term Memory (LSTM) using historical data. In this study, we will discuss the exchange rate prediction of the variant of LSTM, LSTM based on attention. We divide our experiments into three parts, predict currency exchange rates, predict the trends of currency exchange rate, and try to predict exchange rates for a longer day. To better predict the exchange rate of the Australian dollar against the US dollar, we have further added the sentiment analysis of the news articles based on SnowNLP library as well as simple keyword matching on news articles that mention the rise in the Australian dollar. We also compare the performance of using different sizes of input features, such as 7 days, 30 days and 60 days, as well as the different combinations of features, such as historical data, differences and ratios. In addition, we use two different strategies to predict a farther future and compare the performance. |
Our experimental results show that adding sentiment analysis of the news articles as features can reduce prediction error by at least 15\%. The exchange rate prediction performance of using rate ratio is reduced by the test error of 12\%, compared to the performance using only historical data, but the use of the difference as our feature does not contribute to the prediction. The performance of using 7-day input is superior to the other inputs. Finally, we compared the exchange rate prediction performance of different methods, LSTM based on attention with news sentiment analysis outperforms other methods.
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