Abstract
In order to solve the low prediction accuracy and prediction rate of traditional time series prediction algorithm in the analysis of massive data, according to the characteristics of communication network performance index, time series prediction algorithm based on the big data method is proposed. In addition, based on the traditional three components of time series feature extraction, the concept of emergency component is introduced, and the outlier detection and processing as well as prediction analysis are carried out on the basis of the extraction results. The results show that the algorithm based on big data, through the analysis, fitting, modelling, and prediction of massive data, smaller granularity decomposition of the time series value is conducted, which significantly improved the credibility and accuracy of prediction. Based on the above findings, it is summarized that the time series prediction algorithm has good performance.
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Abdollahzade, M., Miranian, A., Hassani, H., et al. (2015). A new hybrid enhanced local linear neuro-fuzzy model based on the optimized singular spectrum analysis and its application for nonlinear and chaotic time series forecasting. Information Sciences, 295, 107–125.
Tratar, L., & Strmčnik, E. (2016). The comparison of Holt–Winters method and multiple regression method: A case study. Energy, 109, 266–276.
Zheng, Z., & Zheng, Z. (2017). Towards an improved heuristic genetic algorithm for static content delivery in cloud storage. Computers & Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2017.06.011.
Sudheer, G., & Suseelatha, A. (2015). Short term load forecasting using wavelet transform combined with Holt–Winters and weighted nearest neighbor models. International Journal of Electrical Power & Energy Systems, 64, 340–346.
Li, C., & Chiang, T.-W. (2013). Complex neurofuzzy ARIMA forecasting—A new approach using complex fuzzy sets. IEEE Transactions on Fuzzy Systems, 21(3), 567–584.
Zheng, Z., Huang, T., Zhang, H., et al. (2016). Towards a resource migration method in cloud computing based on node failure rule. Journal of Intelligent & Fuzzy Systems, 31(5), 2611–2618.
Wang, L., Zeng, Y., & Chen, T. (2015). Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Systems with Applications, 42(2), 855–863.
Shi, X., Zheng, Z., Zhou, Y., Jin, H., He, L., Liu, B., & Hua, Q.-S. (2017). Graph processing on GPUs: A survey. ACM Computing Surveys, 50(6), Article 81.
Kuremoto, T., Kimura, S., Kobayashi, K., et al. (2014). Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing, 137, 47–56.
Zheng, Z., Jeong, H. Y., Huang, T., et al. (2017). KDE based outlier detection on distributed data streams in multimedia network. Multimedia Tools and Applications, 76(17), 18027–18045. https://doi.org/10.1007/s11042-016-3681-y.
Kourentzes, N., Barrow, D. K., & Crone, S. F. (2014). Neural network ensemble operators for time series forecasting. Expert Systems with Applications, 41(9), 4235–4244.
Askari, S., & Montazerin, N. (2015). A high-order multi-variable fuzzy time series forecasting algorithm based on fuzzy clustering. Expert Systems with Applications, 42(4), 2121–2135.
Claveria, O., & Torra, S. (2014). Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Economic Modelling, 36, 220–228.
Sang, Y.-F. (2012). A review on the applications of wavelet transform in hydrology time series analysis. Atmospheric Research, 122, 8–15.
Acknowledgements
We thank the anonymous reviewers and the editors for the valuable feedback on earlier versions of this paper. This paper is supported by the National Statistical Science Research Project of China, under Grant Number 2015LY43.
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Wang, T., Wang, M. Communication Network Time Series Prediction Algorithm Based on Big Data Method. Wireless Pers Commun 102, 1041–1056 (2018). https://doi.org/10.1007/s11277-017-5138-7
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DOI: https://doi.org/10.1007/s11277-017-5138-7