Recycling price prediction of renewable resources

In the renewable resource recycling market, the recycling price is a key factor which can influence the recycling market. The price prediction of renewable resources is important, which is helpful to guide the development of the market. However, it is difficult to make accurate predictions because the data of recycling prices is highly random and complex. Moreover, there is a time lag between the predicted prices and the accurate prices. In this paper, we propose a combined model to solve these problems. Our model decomposes the prediction into two parts: trend price prediction with Moving Average (MA) and residual price prediction with Empirical Mode Decomposition (EMD) and Long Short-Term Memory (LSTM) neural network. Evaluations on a real-world dataset show that our model outperforms those classical prediction models with the error reduced by over 70% and solves the time lag problem.