Method for predicting data and equipment

Abstract

The invention provides a method for predicting data and an equipment. The method comprises the following steps of: reading a plurality of first data related to a first period which contains a plurality of first sub periods, wherein a plurality of first data and a plurality of first sub periods are in a one-to-one correspondence relationship; based on a plurality of first data and a plurality of first sub periods, determining a plurality of first trend values which are in a one-to-one correspondence relationship with a plurality of first sub periods; based on a plurality of first data and a plurality of first trend values, obtaining a compensation sequence constituted by a plurality of compensation amount, wherein a plurality of compensation amount and a plurality of first sub periods are in a one-to-one correspondence relationship; based on the compensation sequence, obtaining estimation values of the compensation dosage which corresponds to a second period by utilizing a support vector machine, wherein the time of the second period is later than that of the first period; based on the estimation values of compensation amount, predicting a second data related to the second period. The embodiment of the invention has effective prediction, thereby being capable of bringing considerable economic benefits for operators.

Description

technical field [0001] Embodiments of the present invention relate to a data prediction method and a data prediction device, and more specifically, to a data prediction method and a data prediction device using a support vector machine (SVM; support vector machine). Background technique [0002] In recent years, wireless communication services have developed rapidly all over the world, the scale of users has continued to expand, and many new services have emerged one after another. However, the increasing traffic volume (showing an obvious upward trend) and the continuous improvement of users' requirements for wireless communication quality have put forward new requirements for wireless communication networks. and so on increase the difficulty. Especially during special holidays (such as Spring Festival, Mid-Autumn Festival, May Day, National Day, New Year's Day, Easter, Thanksgiving, Holy Day, New Year, etc.) or special events (such as Wenchuan Earthquake, Shanghai license...

AI Technical Summary

Problems solved by technology

[0026] The above shortcomings have caused great limitations in the prediction accuracy of linear regression and exponential regression

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