Method for predicting telephone traffic based on clustering and autoregressive integrated moving average (ARIMA) model

Abstract

The invention discloses a method for predicting telephone traffic based on clustering and an autoregressive integrated moving average (ARIMA) model, which belongs to the field of mobile communication and is used for solving the problems of high subjectivity and incorrect partition caused by the way of partitioning traffic cells according to historical experience of experts during prediction of telephone traffic. The method comprises the following steps of: (1) classifying traffic cells into four types, namely main traffic lines, prosperous business districts, institutions of higher education and residential areas according to priori knowledge; (2) performing preprocessing to obtain the clustering characteristics of each traffic cell, wherein the clustering characteristics comprise a relevant coefficient, a variance, a maximum value, an intermediate value, an average value, a minimum value, a value with the highest occurrence frequency and a standard deviation; (3) performing clustering by a K-MEANS clustering algorithm according to the clustering characteristics of each traffic cell so as to form detailed traffic cell types; and (4) predicting the telephone traffic by using the ARIMA model, wherein the same modeling parameter is selected for the same type of detailed telephone traffic cells.

Description

technical field [0001] The invention relates to a traffic prediction method based on clustering and ARIMA models, and belongs to the field of mobile communication. Background technique [0002] The ARMA model is the most common and important time series model. It is widely used in various industry forecasts, such as stocks, GDP growth, etc., and it is also the most classic time series forecasting method. The principles of these two models are briefly introduced below. [0003] The theoretical basis of ARMA series model 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 that can describe this relationship, and then use The model predicts the future trend of the sequence. [0004] For a linear system, the input white noise sequence a t , output a stationary sequence x t , the input...

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Problems solved by technology

[0034] The purpose of the present invention is to solve the problem of subjectivity and inaccurate division in the way of dividing traffic cells according to the historical experience of experts when forecasting traffic volume, and provides traffic forecasting based on clustering and ARIMA models. Volume Forecasting Method

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