Better Effectiveness of Multi-Integrated Neural Networks: Take Stock Big Data as an Example

With the development of big data, in the financial market, the stock price prediction has many research directions from the perspective of big data. The classical time series prediction model cannot adapt to the high-latitude information of stock data in the era of big data. The development of deep...

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Autores principales:	HangLin Lu, XiuYun Peng
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/fab59245a33c4cca85cb7f809d1e1c76
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spelling	oai:doaj.org-article:fab59245a33c4cca85cb7f809d1e1c762021-11-08T02:36:18ZBetter Effectiveness of Multi-Integrated Neural Networks: Take Stock Big Data as an Example1530-867710.1155/2021/3938409https://doaj.org/article/fab59245a33c4cca85cb7f809d1e1c762021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3938409https://doaj.org/toc/1530-8677With the development of big data, in the financial market, the stock price prediction has many research directions from the perspective of big data. The classical time series prediction model cannot adapt to the high-latitude information of stock data in the era of big data. The development of deep learning provides a new idea for high-latitude stock data prediction. Four neural network models and three integrated learning models form different strategy sets, and the opening price of the next timestamp is predicted by backtracking information over the past 15 days with the characteristics of 12 indexes of the stock. The experimental results show that the prediction effect of the integration model based on the average weight policy and stacking policy is better than that of the single neural network, and the integration model based on stacking policy is expected to have the highest prediction accuracy and the minimum expected error. The accuracy was 80.2%, and the mean square error was 0.024. Compared with the single model, the accuracy is increased by 2%~7%, and the error is reduced by 0.01~0.03. The innovation of this article lies in the traditional machine learning thinking is applied to deep learning, as an individual with a variety of neural network to study, through the integration of learning strategies, fusion for the integration model, the experimental results show that the effect of the integrated model is better than that of a single model, to improve the robustness and accuracy of the model; the performance of the integrated model is more stable. For the utilization of big data resources, the integrated model of neural network has better prediction effect.HangLin LuXiuYun PengHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 HangLin Lu XiuYun Peng Better Effectiveness of Multi-Integrated Neural Networks: Take Stock Big Data as an Example
description	With the development of big data, in the financial market, the stock price prediction has many research directions from the perspective of big data. The classical time series prediction model cannot adapt to the high-latitude information of stock data in the era of big data. The development of deep learning provides a new idea for high-latitude stock data prediction. Four neural network models and three integrated learning models form different strategy sets, and the opening price of the next timestamp is predicted by backtracking information over the past 15 days with the characteristics of 12 indexes of the stock. The experimental results show that the prediction effect of the integration model based on the average weight policy and stacking policy is better than that of the single neural network, and the integration model based on stacking policy is expected to have the highest prediction accuracy and the minimum expected error. The accuracy was 80.2%, and the mean square error was 0.024. Compared with the single model, the accuracy is increased by 2%~7%, and the error is reduced by 0.01~0.03. The innovation of this article lies in the traditional machine learning thinking is applied to deep learning, as an individual with a variety of neural network to study, through the integration of learning strategies, fusion for the integration model, the experimental results show that the effect of the integrated model is better than that of a single model, to improve the robustness and accuracy of the model; the performance of the integrated model is more stable. For the utilization of big data resources, the integrated model of neural network has better prediction effect.
format	article
author	HangLin Lu XiuYun Peng
author_facet	HangLin Lu XiuYun Peng
author_sort	HangLin Lu
title	Better Effectiveness of Multi-Integrated Neural Networks: Take Stock Big Data as an Example
title_short	Better Effectiveness of Multi-Integrated Neural Networks: Take Stock Big Data as an Example
title_full	Better Effectiveness of Multi-Integrated Neural Networks: Take Stock Big Data as an Example
title_fullStr	Better Effectiveness of Multi-Integrated Neural Networks: Take Stock Big Data as an Example
title_full_unstemmed	Better Effectiveness of Multi-Integrated Neural Networks: Take Stock Big Data as an Example
title_sort	better effectiveness of multi-integrated neural networks: take stock big data as an example
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/fab59245a33c4cca85cb7f809d1e1c76
work_keys_str_mv	AT hanglinlu bettereffectivenessofmultiintegratedneuralnetworkstakestockbigdataasanexample AT xiuyunpeng bettereffectivenessofmultiintegratedneuralnetworkstakestockbigdataasanexample
_version_	1718443153451646976

Better Effectiveness of Multi-Integrated Neural Networks: Take Stock Big Data as an Example

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