Research Area:  Big Data

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.

Keywords:  

Author(s) Name:  Tao Wang and Minghui Wang

Publisher name:  Springer

DOI:  10.1007/s11277-017-5138-7

Volume Information:  volume 102, pages 1041–1056 (2018)

Paper Link:   https://link.springer.com/article/10.1007/s11277-017-5138-7