Proper model selection for predicting statio-temporal time series data of rice and rubber

Abstract

The objectives of this research were to propose a model with some functions including CAR spatial effects and to select a proper model for predicting rince and rubber yields in southern provices of Thailand. A linear mixed model (LMM) with spatial effects and dummy varables and Fourier seasonal effects was proposed. Bayesian estimation was used and the estimated rice and rubber yields were used for prediction. The dependent variables were rice and rubber yields in southern provinces of Thailand. Factors considered were rain, temperature, soil fertilizer, soil organic matter, trend and also sesonal effects. The results show that rain, temperature, soil fertilizer, soil organic matter, trend and sesonal effects influence on rice and rubber yields. The proposed model with Fourier seasonal term is the most appropriate comparing to the model with dummy variable for season effects and the Holt-Winters model. Therefore, the proposed model with Fourier sesaonal term should be the first consideration for prediction.