The prediction process of traditional time series prediction methods cannot push out the sharing mode on the same data set, while machine learning methods fail to handle nonlinear and large-scale data sets well, and feature engineering needs to be designed manually. The deep learning method makes up for the disadvantage of the traditional prediction method that requires high computation and high manpower, and it uses automatic learning feature engineering instead of manually designed feature engineering. However, prediction methods that only use deep learning make fewer structural assumptions and typically require higher computational resources and a large amount of data to learn accurate models. In response to the above issues, this study proposes to use empirical mode decomposition (EMD) fusing t-tests to divide the sequence into high-frequency and low-frequency components and further process the data using traditional standard template library (STL) sequence decomposition methods for high-frequency components. The high-frequency and low-frequency components are predicted separately by Prophet. The experimental results show that compared with traditional long short-term memory (LSTM) network and Prophet prediction models, the periodic data decomposed by STL sequence can improve the overall prediction accuracy of the model, while the Prophet model fused with EMD greatly improves training efficiency.