Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory
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 netwo...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2020/8831893 |
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| _version_ | 1850157344202162176 |
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| author | Xiaolu Wei Binbin Lei Hongbing Ouyang Qiufeng Wu |
| author_facet | Xiaolu Wei Binbin Lei Hongbing Ouyang Qiufeng Wu |
| author_sort | Xiaolu Wei |
| collection | DOAJ |
| description | 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). |
| format | Article |
| id | doaj-art-39db0cd3d58a47f1807f1b2a50eb1ee5 |
| institution | OA Journals |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-39db0cd3d58a47f1807f1b2a50eb1ee52025-08-20T02:24:13ZengWileyAdvances in Multimedia1687-56801687-56992020-01-01202010.1155/2020/88318938831893Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term MemoryXiaolu Wei0Binbin Lei1Hongbing Ouyang2Qiufeng Wu3Business School, Hubei University, Wuhan 430062, ChinaSchool of Economics and Management, Hanjiang Normal University, Shiyan 442000, ChinaSchool of Economics, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Science, Northeast Agricultual University, Harbin 150038, ChinaThis 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).http://dx.doi.org/10.1155/2020/8831893 |
| spellingShingle | Xiaolu Wei Binbin Lei Hongbing Ouyang Qiufeng Wu Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory Advances in Multimedia |
| title | Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory |
| title_full | Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory |
| title_fullStr | Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory |
| title_full_unstemmed | Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory |
| title_short | Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory |
| title_sort | stock index prices prediction via temporal pattern attention and long short term memory |
| url | http://dx.doi.org/10.1155/2020/8831893 |
| work_keys_str_mv | AT xiaoluwei stockindexpricespredictionviatemporalpatternattentionandlongshorttermmemory AT binbinlei stockindexpricespredictionviatemporalpatternattentionandlongshorttermmemory AT hongbingouyang stockindexpricespredictionviatemporalpatternattentionandlongshorttermmemory AT qiufengwu stockindexpricespredictionviatemporalpatternattentionandlongshorttermmemory |