2022
DOI: 10.1007/s10489-022-03342-5
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Improving exchange rate forecasting via a new deep multimodal fusion model

Abstract: Exchange rates are affected by the impact of disparate types of new information as well as the couplings between these modalities. Previous work mainly predicted exchange rates solely based on market indicators and therefore achieved unsatisfactory results. In response to such an issue, this study develops an inventive multimodal fusion-based long short-term memory (MF-LSTM) model to forecast the USD/CNY exchange rate. Our model consists of two parallel LSTM modules that extract abstract features from each mod… Show more

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Cited by 15 publications
(5 citation statements)
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References 45 publications
(44 reference statements)
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“…Meanwhile, the results of MSE and MAPE, which only used the ANN-BP method, were 13.86345 and 6.28323%. The smaller the MSE value obtained, the better the forecasting performance [36].…”
Section: Resultsmentioning
confidence: 99%
“…Meanwhile, the results of MSE and MAPE, which only used the ANN-BP method, were 13.86345 and 6.28323%. The smaller the MSE value obtained, the better the forecasting performance [36].…”
Section: Resultsmentioning
confidence: 99%
“…Meanwhile, the results of MSE and MAPE, which only used the ANN-BP method, were 13.86345 and 6.28323%. The smaller the MSE value obtained, the better the forecasting performance [21].…”
Section: Resultsmentioning
confidence: 99%
“…Methodology Inspired by deep learning models in time series (Hu et al 2018;Hu and Zheng 2020;Windsor and Cao 2022), we implement a Temporal Attention LSTM with bidirectional encoder representations from transformers (BERT). As shown in Figure 4, the model learns temporally relevant information from sentences, emotions, and numerical data.…”
Section: Multivariate Time Seriesmentioning
confidence: 99%