Construction of Prediction Model for Multi-Feature Fusion Time Sequence Data of Internet of Things Under VR and LSTM

The purpose of the study is to improve the utilization rate of time sequence data generated by the Internet of Things (IoT), and explore their hidden values. Based on the deep neural network of Long Short-Term Memory (LSTM), the prediction model of multi-feature fusion time sequence data under Virtu...

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Autores principales:	Xinwen Liao, Xuyuan Chen
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	VR technology LSTM neural networks time sequence data prediction model Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/b13a11aea13c4f9cb8b64581a13308de
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id	oai:doaj.org-article:b13a11aea13c4f9cb8b64581a13308de
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spelling	oai:doaj.org-article:b13a11aea13c4f9cb8b64581a13308de2021-11-20T00:01:51ZConstruction of Prediction Model for Multi-Feature Fusion Time Sequence Data of Internet of Things Under VR and LSTM2169-353610.1109/ACCESS.2021.3126639https://doaj.org/article/b13a11aea13c4f9cb8b64581a13308de2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9606908/https://doaj.org/toc/2169-3536The purpose of the study is to improve the utilization rate of time sequence data generated by the Internet of Things (IoT), and explore their hidden values. Based on the deep neural network of Long Short-Term Memory (LSTM), the prediction model of multi-feature fusion time sequence data under Virtual Reality (VR) is discussed. First, the application of VR in various fields and the application status of a deep learning algorithm to IoT are analyzed. Second, the preprocessing method of time sequence data of IoT and the demand of deep learning neural networks in predicting time sequence data are analyzed. Based on the above analysis, the prediction model for multi-feature fusion time sequence data of IoT based on the deep learning network of LSTM is proposed. Finally, the experiment are designed to test the performance of the model. The results show that the proposed model and the LSTM-based regression model show high accuracy in the prediction of electrcity consumption data, while the Multi-Layer Perceptron (MLP) regression model has many errors in the prediction of the data. The mean absolute percentage error (Mape) of the proposed model is the lowest, with a percentage of only 2.49%, indicating that the difference between the predicted value and the real value of the proposed model is the smallest. The Mape of the LSTM regression prediction model is 2.57%, only slightly higher than the recommended model. The Mape of the MLP regression model is much higher, with a difference of 9% compared with the real value. The R<sup>2</sup> of the model is 0.873, which is the highest. This study provides a reference for the application of deep learning neural networks in IoT.Xinwen LiaoXuyuan ChenIEEEarticleVR technologyLSTM neural networkstime sequence dataprediction modelElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 153027-153036 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	VR technology LSTM neural networks time sequence data prediction model Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	VR technology LSTM neural networks time sequence data prediction model Electrical engineering. Electronics. Nuclear engineering TK1-9971 Xinwen Liao Xuyuan Chen Construction of Prediction Model for Multi-Feature Fusion Time Sequence Data of Internet of Things Under VR and LSTM
description	The purpose of the study is to improve the utilization rate of time sequence data generated by the Internet of Things (IoT), and explore their hidden values. Based on the deep neural network of Long Short-Term Memory (LSTM), the prediction model of multi-feature fusion time sequence data under Virtual Reality (VR) is discussed. First, the application of VR in various fields and the application status of a deep learning algorithm to IoT are analyzed. Second, the preprocessing method of time sequence data of IoT and the demand of deep learning neural networks in predicting time sequence data are analyzed. Based on the above analysis, the prediction model for multi-feature fusion time sequence data of IoT based on the deep learning network of LSTM is proposed. Finally, the experiment are designed to test the performance of the model. The results show that the proposed model and the LSTM-based regression model show high accuracy in the prediction of electrcity consumption data, while the Multi-Layer Perceptron (MLP) regression model has many errors in the prediction of the data. The mean absolute percentage error (Mape) of the proposed model is the lowest, with a percentage of only 2.49%, indicating that the difference between the predicted value and the real value of the proposed model is the smallest. The Mape of the LSTM regression prediction model is 2.57%, only slightly higher than the recommended model. The Mape of the MLP regression model is much higher, with a difference of 9% compared with the real value. The R<sup>2</sup> of the model is 0.873, which is the highest. This study provides a reference for the application of deep learning neural networks in IoT.
format	article
author	Xinwen Liao Xuyuan Chen
author_facet	Xinwen Liao Xuyuan Chen
author_sort	Xinwen Liao
title	Construction of Prediction Model for Multi-Feature Fusion Time Sequence Data of Internet of Things Under VR and LSTM
title_short	Construction of Prediction Model for Multi-Feature Fusion Time Sequence Data of Internet of Things Under VR and LSTM
title_full	Construction of Prediction Model for Multi-Feature Fusion Time Sequence Data of Internet of Things Under VR and LSTM
title_fullStr	Construction of Prediction Model for Multi-Feature Fusion Time Sequence Data of Internet of Things Under VR and LSTM
title_full_unstemmed	Construction of Prediction Model for Multi-Feature Fusion Time Sequence Data of Internet of Things Under VR and LSTM
title_sort	construction of prediction model for multi-feature fusion time sequence data of internet of things under vr and lstm
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/b13a11aea13c4f9cb8b64581a13308de
work_keys_str_mv	AT xinwenliao constructionofpredictionmodelformultifeaturefusiontimesequencedataofinternetofthingsundervrandlstm AT xuyuanchen constructionofpredictionmodelformultifeaturefusiontimesequencedataofinternetofthingsundervrandlstm
_version_	1718419857380212736

Construction of Prediction Model for Multi-Feature Fusion Time Sequence Data of Internet of Things Under VR and LSTM

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