Method and system for forecasting data

Abstract

The invention discloses a method for forecasting data. The method comprises the following steps of: acquiring a trend value and a residual value of each observation point; determining the principal direction of data forecasting; calculating the distances between one observation point and other observation points one by one, and included angles between the connection lines between one observation point and other observation points and the principal direction; classifying the observation points; calculating the average residual sum of squares of each type of the observation points respectively according to the residual values respectively; and calculating the distance between a target point and each observation point and an included angle between the connection line between the target point and each observation point and the principal direction, selecting the average residual sums of squares corresponding to the distances and the included angles, and forecasting the observed value of the target point. By applying the technical scheme, the distance correlation and the direction correlation are considered comprehensively in the data forecasting process, the accuracy of the average residual sum of squares is improved relative to Kriging interpolation in which only the distance correlation is considered, so that accuracy of the observed value forecasting of the target point is improved.

Description

technical field [0001] The present invention relates to the technical field of data mining, and more specifically, to a data testing method and system. Background technique [0002] With the growing demand for spatial data analysis in recent years, spatial statistics have become more and more widely used in many different fields. In epidemiological research, spatial statistics designate the spatial distribution map of the disease based on the epidemic occurrence data in the target area, find out the specificity of the disease, discover the high-incidence area and the detailed characteristics of the distribution, provide clues for the study of the cause of the disease, and provide clues for the control and provide an important basis for strategies to prevent epidemics. In terms of geological exploration, spatial statistics are mainly used to study spatial structure and conduct spatial estimation. During the prospecting process, considering that the structure of mineral depo...

AI Technical Summary

Problems solved by technology

However, the shortcoming of kriging interpolation method is that it simplifies its spatial correlation with different observation points to the correlation of distance, but in practice, the spatial correlation between the target point and different observation points is not only related to distance , there is also direction correlation. For example, the growth of a certain tree in the forest is often concentrated in a certain direction due to the influence of terrain and light factors.

Therefore, the correlation between the target point and the direction of different observation points leads to a decrease in the prediction accuracy of the kriging interpolation method in the data prediction

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