Forecasting of Particleboard Consumption in Iran Using Univariate Time Series Models

Abstract

The performance of the Autoregressive Integrated Moving Average )ARIMA) model and Double and Holt-winters exponential smoothing techniques for forecasting the consumption of particleboard in Iran are compared. Annual time series data from 1978 to 2009 in the modeling process, and observations from 2010 to 2012 were used to check the accuracy of the models’ forecasting performance. Also, the models’ performances were calculated in terms of RMSE criterion, and the consumption of particleboard in Iran was forecasted up to the year 2017 using the most appropriate model. The results of comparing different forecast models showed that the ARMA (2,1) model yielded the lowest RMSE value compared to the other two models, which makes it more appropriate for the prediction of consumption of particleboard in Iran. Results also revealed that there might be an increasing trend in the consumption of particleboard, i.e., an average annual increasing rate calculated as 5% for particleboard. Thus, it was predicted that the consumption of particleboard would increase from 901,652 m3 in 2012 to 1,178,320 m3 in 2017.

Keywords

• Forecasting
• Particleboard consumption
• Double exponential smoothing
• ARIMA
• Holt-Winters exponential smoothing