Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory
Joint Authors
Wei, Xiaolu
Lei, Binbin
Ouyang, Hongbing
Wu, Qiufeng
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-11
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
This study attempts to predict stock index prices using multivariate time series analysis.
The study’s motivation is based on the notion that datasets of stock index prices involve weak periodic patterns, long-term and short-term information, for which traditional approaches and current neural networks such as Autoregressive models and Support Vector Machine (SVM) may fail.
This study applied Temporal Pattern Attention and Long-Short-Term Memory (TPA-LSTM) for prediction to overcome the issue.
The results show that stock index prices prediction through the TPA-LSTM algorithm could achieve better prediction performance over traditional deep neural networks, such as recurrent neural network (RNN), convolutional neural network (CNN), and long and short-term time series network (LSTNet).
American Psychological Association (APA)
Wei, Xiaolu& Lei, Binbin& Ouyang, Hongbing& Wu, Qiufeng. 2020. Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory. Advances in Multimedia،Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1126716
Modern Language Association (MLA)
Wei, Xiaolu…[et al.]. Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory. Advances in Multimedia No. 2020 (2020), pp.1-7.
https://search.emarefa.net/detail/BIM-1126716
American Medical Association (AMA)
Wei, Xiaolu& Lei, Binbin& Ouyang, Hongbing& Wu, Qiufeng. Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory. Advances in Multimedia. 2020. Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1126716
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126716