基于反正切函数权值累加生成的DGM(1,1)_(atan)模型及其应用

传统DGM(1,1)模型的累加生成算子没有考虑数据振荡对数据序列发展趋势的影响,模型预测结果往往呈现齐次指数增长的趋势.该局限性使得DGM(1,1)模型不适用于本身存在随机振动特征的序列分析与预测.针对这一问题,本文提出基于原始数据均值像序列的随机波动特征分析方法,设计出均值像反正切函数变权形式的累加生成算子;在此基础上建立了基于均值像反正切函数变权累加的DGM(1,1)atan模型,该模型综合考虑了数据的整体增长趋势与局部波动特征;最后,将模型应用于海域水质监测的数据分析与预测,预测结果验证了模型的有效性及实用性.

The DGM(1,1)atan model with variable weight based on arc-tangent function and its application

The accumulative generation operator of traditional DGM(1,1) model does not consider that data volatility will affect the data development trend. This limitation makes a homogenous exponential growth prediction model difficult to simulate stochastic oscillation sequence. To solve this problem, this paper proposes a stochastic oscillation analysis method based on the mean value sequence of raw data, and design a new accumulative operator using trigonometric function values during its weight generation. Based on this accumulative operator, a new DGM(1,1)_atan model with variable weights is provided. Finally, this model is used to forecast data in field of marine water quality monitoring. Result shows the effectiveness and practicability of the proposed model.