Intelligent modeling of abnormal demand forecasting for medical consumables in smart city

Abstract

Abstract Accurate demand forecasting is critical and difficult for managers, especially for complex demand patterns. In this paper, we develop methods for demand forecasting of sparse, transient and erratic medical consumables. Firstly, combining statistical learning of historical data with basic linear regression, price discount estimates are proposed. To reduce sparse estimates, the transformation of historical demand data is added to the linear regression model. Secondly, some general methods are proposed to deal with demand patterns that we cannot clearly capture. Thirdly, we propose optimized model specifications to select optimal model and reduce redundant variables to avoid underfitting or overfitting. In the last, some numerical experiments are carried out based on the model we propose and some completive models in the actual demand data set. In this study, we develop the most realistic price discount response function based on the problem background, which can further improve demand forecasting performance. This paper also discusses many interesting findings and conclusions.