Prediction model construction method, test method, device and system

Abstract

The invention discloses a prediction model construction method, a prediction model test method, a prediction model test device and a prediction model test system. The prediction model construction method comprises the steps of constructing a neural network model comprising an input node and an output node according to pre-collected sample data; determining a correlation coefficient between an output value of a non-output node and a theoretical output value of the output node based on the sample data; adding an intermediate node and / or inter-node connection in the neural network model based onthe correlation coefficient to obtain an evolved neural network model; and generating the prediction model based on the evolved neural network model. According to the method, a relatively good fittingeffect and a relatively good generalization effect are achieved at the same time by using a minimum model structure.

Description

technical field [0001] The invention relates to a construction method, a testing method, a device and a system for a prediction model. Background technique [0002] In daily business, we often encounter the problem of predicting the characteristic quantity in a certain period of time in the future, such as predicting the road flow in the future, predicting the traffic flow and the flow of people in the airport or port in the future, and predicting the traffic flow in the future The traffic flow and the flow of people migrating between various areas of the inner city, etc. [0003] Taking the forecast of the traffic flow of a certain road in the future as an example, there will be many factors affecting the future traffic flow, for example, it is related to the traffic flow of the current road in the previous period, the traffic before and after the same time period of the previous day, and the adjacent traffic flow. It is related to the current traffic of the road, and it m...

AI Technical Summary

Problems solved by technology

[0005] For linear models such as logistic regression (LR), if there is a relatively large nonlinear relationship in the data law, it is easy to underfit;

[0006] Models such as tree structure and general neural network structure can fit possible nonlinear relationships, but if the model control or parameters are improper, or there are too many parameters, it is easy to overfit

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