基于优化的非等间距灰色模型对健康的预测

Prediction of Health Index Based on Improved Non-equidistant Grey Model

针对通过有限的不等时间间隔的健康数据预测未来健康情况难度较大,传统的非等间距灰色预测模型在应用中精度偏低的问题,本文提出一种优化的非等间距灰色马尔科夫预测模型。首先,该模型通过数据预处理和优化预测流程降低数据突变对预测结果造成的影响;其次,设计最佳权重系数来优化模型的构建;最后,采用灰色和马尔科夫修正相结合的策略对残差进行修正。经过实例和对比分析,结果表明,该优化模型具有更高的预测精度,从而可以相对准确地预测短期健康情况。

Aiming at the problems that it is difficult to predict the future health situation through the finite health data with unequal time interval sampling and the accuracy of the traditional non-equidistant grey prediction model is low in the short-term health prediction,an improved non-equidistant grey Markov prediction model is proposed.Firstly,the improved model reduces the impact of data mutation on the prediction results through data preprocessing and optimization of the prediction process.Secondly,the optimal weight coefficient is designed to optimize the model construction.Finally,the residual error is corrected by the strategy of grey and Markov correction.After the comparative analysis on actual monitoring data,the results show that the proposed model has higher prediction accuracy,so that the short-term health situation can be predicted relatively accurately.