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Communication Network Time Series Prediction Algorithm Based on Big Data Method

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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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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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Correspondence to Tao Wang.

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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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