Abstract
In order to study how massive data with periodic and tidal performance influences identification and processing in present network, this paper proposes a data-processing method combined with deep learning algorithm, XGBoost algorithm and spatio-temporal network model. By collecting a large amount of data from existing network and dealing it with pre-treatment, such as anomaly detection and periodicity detection, this proposed data processing method is capable of weeding out erroneous data and fulfill whole data set. In this way, we are capable of getting some characterized results and forming a result set. XGBoost algorithm and space-temporal network model, can then help with data analysis and providing reference for subsequent research such as feasibility analysis and scheme design. This scheme proposed in this paper is able to work in an effective way, especially when facing problems composed of large amount of data with periodicity.
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References
Mikolov, T., Chen, K.: Efficient estimation of word representations in vector space. Comput. Sci. (2013)
Wang, S.-H., Li, H.-T., Chang, E.-J., (Andy) Wu, A.-Y.: Entropy-assisted emotion recognition of valence and arousal using XGBoost classifier. In: Iliadis, L., Maglogiannis, I., Plagianakos, V. (eds.) AIAI 2018. IAICT, vol. 519, pp. 249–260. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92007-8_22
Chen, T.Q., He, T., Benesty, M.: XGBoost: Extreme Gradient Boosting (2016)
Li, V., Costantino, H., Rowland, J.: Ml3 lasso (least absolute shrinkage and selection operator) and XGBoost (extreme gradient boosting) models for predicting depression-related work impairment in us working adults. Value Health 24, S11 (2021)
**n, D.X.H.: Receding horizon path planning of automated guided vehicles using a time-space network model. Optim. Control Appl. Methods 41(6), 1889–1903 (2020)
Dimitrijevic, M., Andrejevic-Stosovic, M.: Implementation of artificial neural networks based AI concepts to the smart grid. Electron. Energ. 27(27), 411–424 (2017)
Li, S., Shang, Q., Tian, B.: Research on pavement skid resistance performance prediction model based on big data analysis and XGBoost algorithm. In: International Conference on Fuzzy Systems and Data Mining (2019)
Han, S., Ko, Y.: Enhancement of prediction accuracy for home sales index prediction model based on integration of multiple regression analysis and genetic algorithm. KSCE J. Civ. Eng. 22(7), 2159–2166 (2018)
Xu, L., et al.: Cell load and resource-aware flow shifting scheme based on heterogeneous mobile networks data. In: 7th IEEE International Conference on Information and Communication Technologies for Disaster Management, pp. 93–100, IEEE, Hangzhou (2021)
Xu, L., et al.: Architecture and technology of multi-source heterogeneous data system for telecom operator. In: 7th International Conference on Signal and Information Processing, Networking and Computers, pp. 1000–1009. Springer, Rizhao (2020)
Xu, L., Cheng, X., et al.: Mobility load balancing aware radio resource allocation scheme for LTE-advanced cellular networks. In: 16th IEEE International Conference on Communication Technology, pp.806–812. IEEE, Hangzhou (2015)
Wang, C., et al.: Time synchronization and signal detection in non-orthogonal unicast and broadcast networks. IEEE Trans. Broadcast. 69(2), 635–646 (2023)
Wang, C., et al.: Energy-efficient task scheduling based on traffic map** in heterogeneous mobile edge computing: a green IoT perspective. IEEE Trans. Green Commun. Netw. 7(2), 972–982 (2023)
Wang, C., et al.: Multimodal semantic communication accelerated bidirectional caching for 6G MEC. Futur. Gener. Comput. Syst. 140, 225–237 (2023)
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Xu, Z. et al. (2024). Time Series Processing Scheme Based on Deep Learning and Spatio-temporal Network Modeling. In: Wang, Y., Zou, J., Xu, L., Ling, Z., Cheng, X. (eds) Signal and Information Processing, Networking and Computers. ICSINC 2023. Lecture Notes in Electrical Engineering, vol 1188. Springer, Singapore. https://doi.org/10.1007/978-981-97-2124-5_59
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DOI: https://doi.org/10.1007/978-981-97-2124-5_59
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