Time Series Forecasting for Density of Wood Growth Ring using ARIMA and Neural Networks - Ming-Bao Li; Jia-Wei Zhang; Shi-Qiang Zheng (2007 International Conference on Machine Learning and Cybernetics, 2007)
Wood density is one of the most important wood characteristics which determine final wood product qualities and properties. In this article, ARIMA, multilayer perceptron (MLP), and particle swarm optimization BP (PSO-BP) network models are considered along with various combinations of these models for forecasting density of wood growth ring. The forecasting principle and procedure of these three methods are presented. Measurement experiments are carried out to get the time series data of wood density. Simulation comparison of forecasting performances shows that the neural network models with particle swarm optimization give a better performance in solving the wood density forecasting problem.