Network index prediction method and device based on ARIMA model and storage medium

Abstract

The invention discloses a network index prediction method and device based on ARIMA model and a storage medium. The method comprises the steps of: collecting index data of a to-be-predicted index variable in a certain time period as a training data set; taking the pre-processed training data set as input to construct an ARIMA model of network index prediction; and commonly inputting the future time length of the set to-be-predicted index variable and the selected stationary series having passing d-order difference into the ARIMA model, and calculating and obtaining a target prediction value. The change rules of users for wireless network demands are subjected to statistics to predict the change sequence value of an index in a certain time in the future so as to provide more effective datareference for optimizing wireless network resource allocation and performance optimization.

Description

technical field [0001] The present invention relates to the technical field of data mining, in particular to a network index prediction method, device and storage medium based on an ARIMA model. Background technique [0002] With the rapid development of communication and information technology and the large-scale popularization of wireless networks, the demand for the network from the huge number of users has forced traditional operation and maintenance methods to seek more efficient data decision-making methods. How to quantify and predict users' usage of the network in advance and accurately, avoid or reduce the probability of network congestion, and improve resource allocation efficiency is one of the key contents of daily operation and maintenance optimization for communication operators. [0003] At present, the method of predicting and discovering network communication problems in advance is lacking, and the post-event analysis of problems is often mainly based on sim...

AI Technical Summary

Problems solved by technology

[0005] 1) Commonly used methods such as linear prediction methods and neural networks are less practical, and the prediction results are unstable under small samples, and cannot be well compatible with the changing laws of time series;

[0006] 2) It is easy to ignore the periodic changes of indicators

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