An efficient analysis of rice prices and yields in Thailand using optimal functions for seasonality and trend in time series models
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This research aims to apply the principle of stochastic process for modeling. The parameters are estimated using Bayesian methods. The monthly average real price of paddy rice 15% and yield of paddy rice in Thailand were studied. The price and the yield of paddy rice which are time series data consisting of four components, autocorrelation, an exponential cumulative distribution function for trend, outliers, and two different types of seasons: the dummy seasons and Fourier function seasons. Writing algorithms, programming in OpenBUGS and evaluating the performance of models from simulation programming in R were conducted. After that, Bayesian methods with dummy seasons and Fourier function season were compared using RMSE, MSE and MAE as the criteria. The results show that the Bayesian model with an exponential cumulative distribution function for trend and dummy seasons give the lowest RMSE, MSE and MAE for both model fitting and model validating.
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