Multi-data-set joint prediction method based on attention mechanism

Abstract

The invention discloses a multi-data-set joint prediction method based on an attention mechanism, and the method comprises the following steps: 1) carrying out the anomaly detection of different types of activity data in a CDR data set through employing a mobile network anomaly detection method based on feature extraction, removing abnormal activity data, and then respectively putting each type of activity data into a recurrent neural network with the same structure; 2) inputting an output result of each recurrent neural network into an attention unit, and inputting an output result of the attention unit into the recurrent neural network for flow data prediction; and 3) taking the output of the recurrent neural network used for traffic data prediction as a prediction result of the cellular traffic. The method can accurately predict the traffic data at the future moment.

Description

technical field [0001] The invention relates to a prediction method, in particular to a multi-data set joint prediction method based on an attention mechanism. Background technique [0002] Traffic prediction is one of the important tasks in wireless network data analysis and management. Traffic forecasting in wireless networks is a time series forecasting problem. According to the historical traffic data series, the traffic value at a unit time in the future is estimated. Traffic forecasting is very valuable to service providers. Predicting the traffic data at a future moment can provide network operators with more information about hotspot areas, examine the rationality of existing resource allocation schemes, and guide the dynamic allocation and adjustment of network resources. However, the prior art does not provide similar disclosure. Contents of the invention [0003] The purpose of the present invention is to overcome the above-mentioned shortcomings of the prior ...

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Problems solved by technology

Predicting the traffic data in the future can provide network operators with more information about hotspot areas, examine the rationality of existing resource allocation schemes, and guide the dynamic allocation and adjustment of network resources. However, the prior art does not provide similar disclosure.

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