Method for predicting mobile traffic based on LS-SVM

Abstract

The invention relates to a method for predicting mobile traffic based on a least square support vector machine (LS-SVM), belongs to the field of mobile communication, and aims to solve the problems that only single-step traffic prediction can be realized by the LS-SVM, and an algorithm cannot effectively and reasonably select input variables to cause low accuracy and speed in the prior art. The method comprises the following steps of: 1, selecting traffic historical data within a month before the current time as a training sample and performing LS-SVM modeling to acquire an LS-SVM prediction model; 2, preprocessing a newly input sample; 3, inputting the processed newly input sample into the LS-SVM prediction model and outputting a predicted value; 4, judging whether the LS-SVM prediction model needs to be updated or not, if so, returning to the step 1, otherwise, executing the step 5; and 5, taking the predicted value output in the step 3 as traffic data of the current time, returningto execute the step 2, and predicting traffic of next time.

Description

technical field [0001] The invention relates to a method for predicting mobile traffic volume based on LS-SVM, which belongs to the field of mobile communication. Background technique [0002] With the rapid development of wireless communication services, the demand for traffic forecast is increasing. Accurate traffic prediction is of great significance to the operation and management of wireless communication networks. Mobile traffic data is collected every hour, and the unit is Ireland. [0003] At present, the quantitative traffic forecasting methods mainly include traditional time series analysis method, artificial neural network algorithm and support vector machine method. The theoretical basis of time series analysis method modeling is to use the information of the historical data sequence, find the law of the correlation relationship between the sequence values ​​according to the correlation relationship in the data sequence obtained by statistics, and fit a model t...

AI Technical Summary

Problems solved by technology

[0058] The purpose of the present invention is to solve the problem that the prior art using LS-SVM for traffic forecasting can only realize single-step forecasting, and the algorithm itself cannot effectively and reasonably select the input variables, resulting in poor accuracy and slow speed, and provides A mobile traffic prediction method based on LS-SVM

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