Currency Exchange Rate Prediction with Long Short-Term Memory Networks Based on Attention and News Sentiment Analysis

研究成果: 書貢獻/報告類型會議論文篇章同行評審

6 引文 斯高帕斯(Scopus)

摘要

Currency exchange rate prediction is a typical time series prediction problem which has been solved by time-series models, such as Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA) as well as machine learning methods, such as Single Layer Perception (SLP) and Long Short-Term Memory (LSTM). In this work, we aim to predict the future currency exchange prices in collaboration with news sentiment analysis. We use the Australian dollar (AUD) against the US dollar as a case study and study the prediction AUD rate for next day, week, two-week, and month. We conduct a comparative study of the proposed attention-based LSTM with typical models, including ARIMA, SARIMA, SLP, and classical LSTM. The numerical results showed that adding sentiment score of the news articles and matching keywords of 'up/increase' can reduce prediction error by at least 15%. For more extended future prediction, the newly trained model is better than the strategy that reuses the next-day model.

原文???core.languages.en_GB???
主出版物標題Proceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728146669
DOIs
出版狀態已出版 - 11月 2019
事件24th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019 - Kaohsiung, Taiwan
持續時間: 21 11月 201923 11月 2019

出版系列

名字Proceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019

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???event.eventtypes.event.conference???24th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019
國家/地區Taiwan
城市Kaohsiung
期間21/11/1923/11/19

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