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A new grey incidence model, called the grey dynamic trend incidence model GDTIM(t) , is designed to broaden the scope of this type of model. In particular, a degree of the grey trend incidence, an array and a matrix of the grey dynamic trend incidence are obtained from two data sets which include both time series and panel data, by using the GDTIM(t) model. The new model includes two parts. One component is a judgment factor which is used to determine whether the trend of two sequences is the same or different. The other part is a measurement factor for calculating the absolute degree of grey trend incidence. Then, the properties of the model GDTIM(t) are discussed. Two extended models based on GDTIM(t) , which are utilized for application to panel data, are established. One is for calculating the connection between indicators, and the other one is for different objects. The new model is used to dynamically analyze factors connected to smog weather in south Jiangsu province. [ABSTRACT FROM AUTHOR]

Exposure to air pollutants has become a major global health threat and is a dominant contributor to increasing respiratory mortality. Accurate projections of respiratory mortality are crucial to mitigating the adverse influences of air pollution on public health and guiding long-term policy implementation. However, the forecasting models' accuracy is difficult to guarantee because seasonal factors perturb the raw series of air pollutants and respiratory mortality. This paper proposes an enhanced seasonal and self-adaptive multivariate grey convolution model to solve this dilemma. The innovations of the proposed model reside in the following areas: firstly, a seasonal operator vector of multidimensional data is initially built to digitize the seasonality of air pollutants and related mortality. Secondly, a multivariate grey convolution model with an adaptive time-lag cumulative optimization item is put forward. The weight coefficients of each driving variable at different periods are intended to adapt dynamically. Thirdly, the self-adaptive time-delay coefficient is determined by the Particle Swarm Algorithm, enhancing the accuracy of predictions. Theoretically, the generalizability and adaptability of the newly presented model are well-validated. Moreover, its practical applicability and higher forecasting performance are further verified in estimating the respiratory mortality attributable to the comprehensive effects of air pollutants in China. [ABSTRACT FROM AUTHOR]