چکیده (انگلیسی):

Bayesian methods furnish an attractive approach to time series data analysis. This article proposes the forecasting
models that can detect trend, seasonality, auto regression and outliers in time series data related to some
covariates. Cumulative Weibull distribution functions for trend, dummy variables for seasonality, binary
selections for outliers and latent autoregression for autocorrelated time series data are used for the data analysis.
The Gibbs sampling, a Markov Chain Monte Carlo (MCMC) algorithm, is used for the parameter estimation. The
proposed models are applied to vegetable price time series data in Thailand. According to the RMSE, MAPE and
MAE criteria for model comparisons, the proposed models provide the best results compared to the exponential
smoothing models, SARIMA models and the Bayesian models with trend, auto regression and outliers.