Research Article
Intelligent Analysis Method of Multidimensional Time Series Data Based on Deep Learning
@INPROCEEDINGS{10.1007/978-3-031-50571-3_32, author={Zhongwei Chen and Xiaofeng Li}, title={Intelligent Analysis Method of Multidimensional Time Series Data Based on Deep Learning}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part I}, proceedings_a={ICMTEL}, year={2024}, month={2}, keywords={Deep Learning Multi Dimensional Time Series Data Intelligent Analysis Dimension Reduction Processing}, doi={10.1007/978-3-031-50571-3_32} }- Zhongwei Chen
Xiaofeng Li
Year: 2024
Intelligent Analysis Method of Multidimensional Time Series Data Based on Deep Learning
ICMTEL
Springer
DOI: 10.1007/978-3-031-50571-3_32
Abstract
In order to improve the intelligent analysis performance of multidimensional time series data, an intelligent analysis method of multidimensional time series data based on deep learning is proposed. The deep learning network model is used to establish the distribution function of correctly classified and wrongly classified data. According to the calculation process of the deep learning network, the outliers of multi-dimensional time series data are mined. The feature space of multi-dimensional time series data is determined using the k-nearest neighbor method. The multi-dimensional time series data is reduced in dimension by calculating the covariance matrix of multi-dimensional time series data, and the time series data is classified according to the preference level, extract the characteristics of multidimensional time series data, and combine with the design of multidimensional time series data analysis algorithm to achieve intelligent analysis of multidimensional time series data. The experimental results show that the method in this paper can reduce the mean absolute error and root mean square error to within 0.25 and 0.3 respectively in the intelligent analysis of multidimensional time series data, and improve the analysis performance of multidimensional time series data.
