The objective of this research is to propose a forecasting model for uncertain demand index data of steel. The forecasting model proposed by Yelland (2010) was modified by adjusting the prior distributions of some parameters in the model in order that it is suitable for the demand of steel in Thailand. The algorithms for model fitting were written in OpenBUGS. The proposed model was compared to a classical exponential smoothing model. The research found that the forecast errors from the proposed forecasting model were minimum.

The objective of this research is to construct the appropriate forecasting model for the export quantity of concentrated latex by the three time series analysis methods which are Box-Jenkins method Winters’ multiplicative exponential smoothing method and combined forecasting method. Time series data from the website of Office of Agricultural Economics during January 1998 to February 2014 (194 values) are used and divided into two series. The first 182 values from January 1998 until February 2013 for the models prediction and the last 12 values from March 2013 until February 2014 for comparison the performance of forecasting models via the criterion of the lowest root mean squared error and the criterion of the highest correlation coefficient between existing values and forecasted values. Research findings indicated that for all forecasting methods that have been studied the most powerful method is the combined forecasting and the forecasting model is where and represent the single forecasts at time t from Box-Jenkins method and Winters’ multiplicative exponential smoothing method respectively.

The objective of this study was to construct the black pepper prices forecasting model by 4 statistical methods: Box-Jenkins method Holt’s exponential smoothing method Brown’s exponential smoothing method and damped trend exponential smoothing method. Time series of monthly black pepper prices which were gathered from the website of the Office of Agricultural Economics during January 2005 to December 2018 of 168 observations were divided into 2 datasets. The first dataset had 156 observations from January 2005 to December 2017 for constructing the forecasting models. The second dataset had 12 observations from January to December 2018 for comparing the accuracy of the forecasts via the criteria of the lowest mean absolute percentage error (MAPE) and root mean squared error (RMSE). Research findings indicated that for all forecasting methods that had been studied the most accurate method was damped trend exponential smoothing method (MAPE = 6.5773 RMSE = 10.3775)