Wood resource management using an endocrine NARX neural network

Planning and forecasting wood resources implies a challenging analysis, which has a direct impact on planning human resources, production timeline, as well as stock management of wooden assortments, which requires a complex data analysis taking into account all inputs that define the yield of wooden material. This paper includes an analysis of monthly time series data from 1991 to 2015 which can be characterized as long time dependence data. In recent years, artificial neural networks have become a popular tool for time dependence data treatment. Therefore, a prediction of monthly requirements of treated wood is performed by developing a new type of neural network in this research. The nonlinear autoregressive model with exogenous inputs (NARX) is used as a foundation of a new network. NARX is a type of recurrent neural network which is a very effective tool for approximation of any nonlinear function, especially ones which could occur during a nonlinear time sequence prediction. The main contribution of this paper is the introduction of an artificial endocrine factor inside the standard NARX structure. The developed ENARX model provides an extra sensitivity of the network to environmental conditions and external disturbances, as well as its improved adaptive capability. The proposed network shows better forecasting performances compared to the default NARX network, thus establishing itself as an excellent prediction tool in the field of wood science, engineering and technology.