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.
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References
Shaukat, F., Mir, J., Linsen, L., et al.: Interactive visual analytics tool for multidimensional quantitative and categorical data analysis. Inf. Vis. 19(3), 234–246 (2020)
Zhao, P., Chang, X., Wang, M.: A novel multivariate time-series anomaly detection approach using an unsupervised deep neural network. IEEE Access 9(2), 109025–109041 (2021)
Tao, T., Zhou, X., Ma, B., et al.: Abnormal time series data detection of gas station by Seq2Seq model based on bidirectional long short-term memory. J. Comput. Appl. 39(3), 924–929 (2019)
Xia, Y., Han, X.: Multi-dimensional time series data anomaly detection fusing statistical methods and bidirectional convolutional LSTM. Appl. Res. Comput. 39(05), 1362–1367+1409 (2022)
Meng, C., Jiang, X.S., Wei, X.M., et al.: A time convolutional network based outlier detection for multidimensional time series in cyber-physical-social systems. IEEE Access 56(2), 451–455 (2020)
Zhao, L., Shi, Z., Shi, Q., et al.: Network traffic classification method based on time series features. J. North Univ. China (Nat. Sci. Edn.) 43(03), 221–228 (2022)
Lu, W., Shan, D., Pedrycz, W., et al.: Granular fuzzy modeling for multidimensional numeric data: a layered approach based on hyperbox. IEEE Trans. Fuzzy Syst. 27(4), 775–789 (2019)
Fei, G., Shaoxu, S., Jianmin, W.: Time series data cleaning under multi-speed constraints. J. Softw. 32(03), 689–711 (2021)
He, Y., Liu, C., Yang, Z.: Temporal dependency mining from multi-sensor event sequences for predictive maintenance. J. Chin. Comput. Syst. 42(11), 2307–2312 (2021)
Gan, X., Tang, X.: Mining model of association rules for temporal data based on CNN. Comput. Simul. 38(03), 282–285+326 (2021)
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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Chen, Z., Li, X. (2024). Intelligent Analysis Method of Multidimensional Time Series Data Based on Deep Learning. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 532. Springer, Cham. https://doi.org/10.1007/978-3-031-50571-3_32
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DOI: https://doi.org/10.1007/978-3-031-50571-3_32
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