Stochastic Processes for Time Series Models with an Application to Data of Cash and Future Prices of Rice in Thailand

Abstract

The objective of this research is to use a stochastic process for building a model using Bayesian parameter estimation. The model is applied to the of crash prices and future prices of rice in Thailand for 120 moths including monthly closing average price of white rice 5%, non-glutinous paddy 15%, and Hom Mali non-glutinous paddy 105. The data are time series data containing autocorrelation, trend, seasonality, and outliers. The algorithms are turned into codes in OpenBugs and the performance of the model is evaluated using simulation in R. The model with a Weibull cumulative distribution function trend, the model with an exponential cumulative distribution function trend, and the exponential smoothing model are compared. The root mean squared error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) are used as the criteria. The results show that the model with the model with an exponential cumulative distribution function trend gives smallest RMSE, MAPE and MAE for the crash prices of non-glutinous paddy 15% and Hom Mali non-glutinous paddy 105 for both model fitting part and model validation part. The model with a Weibull cumulative distribution function trend gives the smallest RMSE, MAPE and MAE for the closing average price of white rice 5% for both model fitting part and model validation part.