Telephone traffic prediction method based on periodic dependence

Abstract

The invention relates to a telephone traffic prediction method based on periodic dependence. The method comprises steps of collecting background data of a telephone traffic center; carrying out preprocessing to obtain telephone traffic; extracting features of periodic dependence of telephone traffic in different time units, adding feature dimensions to the extracted features, inputting all the features and corresponding target values into an algorithm model, obtaining a stable model after training, and inputting telephone traffic prediction request data into the model to obtain telephone traffic prediction. The method does not depend on a standard sequence model, the periodic dependency relationship of the telephone traffic in different time units is fully considered, and the change trendof the telephone traffic in different time periods can be well reflected; based on big data analysis, model training is carried out by using a machine learning algorithm, the telephone traffic in thefuture time period can be predicted more accurately, and the telephone traffic in each time period in the future for a long time can be predicted.

Description

technical field [0001] The invention relates to telephone communication; the technical field of automatic or semi-automatic switching office, in particular to a method for forecasting traffic volume based on periodic dependence. Background technique [0002] Traffic forecast (traffic forecast) refers to the mathematical method of obtaining the basic information necessary for the medium and long-term development planning of the telecommunication network or for adjusting the organization of each trunk circuit group in the near future. Forecast content traffic forecast can be divided into total forecast and traffic flow forecast two parts, total forecast is based on different requirements of a bureau, a city, a province (district), or the country's traffic total and forecast, and Traffic flow forecasting is to predict the traffic flow from a certain calling office to a certain receiving office, and it has directionality, so it is to predict the distribution of traffic among var...

AI Technical Summary

Problems solved by technology

[0006] 1) Time series modeling and forecasting, the future electricity is modeled and predicted in time series; the defect of this method is that it usually focuses on the modeling of the total daily or even monthly electricity, and relies on the standard sequence model, not in the long-term traffic forecast in each time period;

[0007] 2) Poisson modeling prediction, the call process is modeled as a Poisson process, and the call process of a day is modeled as a non-homogeneous Poisson process; the defect is that it is only modeled for the call volume of the next day, without considering the periodicity Dependent factors, although the forecast is the amount of each time period of the day, it is not long-term;

[0008] 3) Linear fixed, mixed modeling forecasting, which introduces the number of incoming calls in the same time period of the previous few time periods and the same time period of the previous few days for parameter estimation; the defect is that the forecast is modeled by day, and periodic dependence factors are not considered;

[0009] 4) Periodic average forecast, taking the average value according to different periods, such as taking the average value according to the daily cycle; the defect is that the periodic average reflects the overall change trend, and cannot accurately predict changes in the future time period

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