Modeling the price of trends of teak wood using statistical and artificial neural network techniques

Modeling the trends and patterns in financial data is of great interest to the business community to support the decision-making process. In this study, the historical trends in the real prices of teak wood were described using spline models and the time periods for which the rate of change in real prices differed were identified. The possible reasons for this phenomenon such as impact of forest legislations and other factors have been explained. In forecasting teak wood prices, Artificial Neural Network (ANN) was compared with the traditional Auto Regressive Integrated Moving Average (ARIMA) model. The Mean Absolute Percentage Error (MAPE) was lesser in the case of ANN than the ARIMA model. Further, the turning points were more closely predicted by ANN. It appeared that forecast by ANN was heavily dependent on the previous value(s) immediate to the forecasting year. The study concluded that the next year price forecasts by univariate ARIMA and ANN models may be far from actual prices due to unanticipated factors.