Abstract: Drug prediction can ensure that patients get the drugs they need in time. It can also effectively control the spread of infectious diseases. In view of the characteristics of multi-periodicity and high randomness of long time series data of drug demand, this paper proposes a comprehensive prediction model based on multi-decomposition denoising. Firstly, multiple seasonal-trend decomposition using LOESS(MSTL) algorithm and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) are used to decompose the drug demand series for the second time to fully extract the time series features. Secondly, wavelet denoising (WD) and combination reconstruction are carried out for each decomposition component to effectively reduce data noise interference. In the modeling stage, a hybrid prediction model is constructed by combining the feature extraction ability of the convolutional neural network(CNN) and the time series modeling advantage of the long short-term memory(LSTM) network. In order to further improve the performance, the African vultures optimization algorithm(AVOA) is used to automatically optimize the hyperparameters of the model. The experimental results show that the decomposition denoising strategy proposed in this paper can effectively improve the prediction performance of the model and has good versatility.
Keywords: demand prediction; drugs; long-term data; multiple decomposition; time series reconstruction
合肥工业大学学报自科版