Predicting the demand of elderly care beds by a novel recursive grey Gompertz model: case studies of Jiangsu and Shanghai, China

The RGGM model introduces a memory factor into its objective function, which assigns a greater weight to newer observations. This mechanism ensures the priority of new information during the modeling process, enhancing the model's adaptability. Furthermore, the model's structural parameters are solved recursively with each new observation, allowing the parameters to inherit information from previous data points. This recursive approach effectively improves prediction accuracy. Accurately predicting the supply of and demand for elderly care resources can assist governments and policymakers in formulating rational elderly service plans, optimizing resource allocation, and ensuring the sustainable development of the elderly care system.