Application of a hybrid model based on the Prophet model, ICEEMDAN and multi-model optimization error correction in metal price prediction

Abstract

Nonferrous metals are the basic materials and key strategic reserve resources for national economic development. The fluctuation of metal prices seriously affects the development of modern industry and the global economy. Therefore, accurate and stable nonferrous metal price prediction is very important and meaningful. In this paper, a relatively novel prediction system is proposed to predict the price of metals. The hybrid model of the Prophet model, ICEEMDAN and multi-model optimization error correction is applied to predict the prices of the metals zinc, aluminum, copper and gold. The Prophet model can predict the data more concisely by combining the research background and statistical knowledge, but there are also some nonlinear residual sequences in the prediction. Therefore, ICEEMDAN is used to decompose the residual sequence, and ARIMA, ELMAN, BPNN, LSTM and NAR are applied to predict the decomposed subsequences. Then, the optimal prediction results in each subsequence are selected to add the residual prediction value. Finally, the prediction value of the original price is obtained by adding the Prophet model and the residual prediction value. The price series of four metals from the London Metal Exchange and Investing are selected as the research object. Through two groups of experiments, the proposed model is compared with six single models and six hybrid models. At the same time, to further verify the performance and the reliability of the proposed model, DM detection and model confidence set trimming are used. Through the evaluation index and the performance test of the model, the proposed model's superiority and reliability in metal price prediction are effectively proven.