{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T05:21:23Z","timestamp":1648963283490},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,15]]},"abstract":"<jats:p>Recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks have shown some success with many practical applications in recent years such as machine translation, speech recognition, image processing and financial market forecasting. In recent years, a dual-stage attention-based recurrent neural network (DA-RNN) has shown some promising results on stock price prediction. We propose dual attention-dilated long short-term memory (DAD-LSTM) models combining DA-RNN and dilated recurrent neural networks (DRNN) to select the most relevant input features and capture the long-term temporal dependencies of a time series more efficiently. Numerical results from experiments on the NASDAQ 100, S&amp;P 500, HSI and DJIA datasets show that DAD-LSTM models outperform the state-of-the-art and most recent approaches.<\/jats:p>","DOI":"10.3233\/faia200589","type":"book-chapter","created":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T23:15:46Z","timestamp":1600298146000},"source":"Crossref","is-referenced-by-count":0,"title":["An Efficient Hybrid Mechanism with LSTM Neural Networks in Application to Stock Price Forecasting"],"prefix":"10.3233","author":[{"given":"Ngoc-An","family":"Nguyen-Pham","sequence":"first","affiliation":[{"name":"AISIA Research Lab, Ho Chi Minh City, Vietnam"}]},{"given":"Trung T.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"University of Science, Ho Chi Minh City, Vietnam"},{"name":"Vietnam National University in Ho Chi Minh City, Vietnam"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques"],"original-title":[],"link":[{"URL":"http:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA200589","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T13:43:30Z","timestamp":1600350210000},"score":1,"resource":{"primary":{"URL":"http:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA200589"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,15]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia200589","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,15]]}}}