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

IEEE Access

Xuyuan Chen¹  Xinwen Liao² 

[1]Center for Teaching and Learning Development, Southern Medical University, Guangzhou, China

[2]School of Journalism and Communication, Sichuan International Studies University, Chongqing, China

关键词: VR technology;    LSTM neural networks;    time sequence data;    prediction model;   

DOI  :  10.1109/ACCESS.2021.3126639

来源: DOAJ

【 摘 要 】

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 R² 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.

【 授权许可】

Unknown