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: Xiaolu Wei, Binbin Lei, Hongbing Ouyang, Qiufeng Wu
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Advances in Multimedia
Online Access:http://dx.doi.org/10.1155/2020/8831893
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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).
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institution OA Journals
issn 1687-5680
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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