Short-term temperature prediction based on hybrid CEEMDAN, neural networks, and linear regression methods

Abstract

Weather prediction is one problem of great importance in social life. To predict the future temperature with past data and without computationally intense physical modeling, a hybrid machine learning-based prediction model that combines the empirical mode decomposition (EMD), linear regression, and two different neural networks is proposed in this project. Since the temperature is a timeseries data, the periodic patterns in the data are extracted into intrinsic mode functions (IMFs) with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the future values of individual IMFs are forecasted by learning past patterns with the long short-term memory (LSTM) and multilayer perceptron (MLP) models for each IMFs. Linear regression is also used to predict the change in the nonperiodic trend. The predictions are added together to construct the result. By comparing with the actual test set results and the errors of other models, experiments show that this proposed model displays good performance in temperature prediction.