Service flow prediction method based on network big data analysis

Abstract

The invention discloses a service flow prediction method based on network big data analysis, and belongs to the field of communication network big data analysis. According to the method, remote dependence and irregular fluctuation behaviors of the service flow under a big data background are independently considered, and the service flow is divided into two components marked by scaling and discrete wavelet transform coefficients by utilizing discrete wavelet transform. Discrete wavelet transform looks like a filter, and the service flow is decomposed into a low-pass component and a high-pass component, wherein the low-pass component represents the long-term dependence of the service flow, and the high-pass component represents the strong and irregular fluctuation of the service flow. For short-term and irregular fluctuations, it is assumed that they comply with Gaussian distribution consisting of expectations and variances; the parameters are calculated through the maximum likelihood estimation of the known service flow data; and by adopting the method, the problems of service flow characteristics, random addition of users, complex association and the like under the background of complex network big data can be effectively solved.

Description

technical field [0001] The invention belongs to the technical field of business flow modeling and forecasting in communication network big data analysis, and in particular relates to a business flow forecasting method based on network big data analysis. Background technique [0002] With the high development of information technology, the amount of data accumulated by people is increasing day by day, how to quickly obtain effective data from massive data has become a top priority. Big data refers to data sets that cannot be retrieved and managed using conventional tools under current conditions. Network business traffic has the characteristics of large data volume, various data types, low value density, and fast processing speed. Deep learning under network big data analysis is of great significance to the research of big data predictive analysis for management and decision-making. Compared with wireless ad hoc networks, wireless mesh networks based on network big data anal...

AI Technical Summary

Problems solved by technology

[0003] At present, with the diversification of network services and applications, the amount of data involved is increasing exponentially, and business traffic shows long-term dependence on terminal behavior characteristics, multi-fractal characteristics, and some nonlinear characteristics, which appear very complicated. Many proposed methods are not suitable for traffic forecasting in wireless mesh backbone networks

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