Finite Mixture Model: Prediction of Time Series Data Using Bayesian Method

Abstract

The aim of this study is to measure the number of components that exhibits from the variables' series. The number of components can be affected by the time series components including trend, seasonal adjustment, and irregular changes. By using a finite mixture model, the number of components can be identifies and thereafter we can formulate a Bayesian regression equation to predict the relationship between exchange rate and international tourism expenditure in Malaysia. Identification of the number of components is an important step to weigh the probability density function for a time series data. The weight of the probability density function is then used for prediction. Besides, a Bayesian method is also used in this study to fit with the finite mixture model due to its consistency characteristic. The Bayesian parameter estimates are close to the predictive distributions because it will integrate the prior distribution with the likelihood function to produce posterior distribution. The results show that there is a two-component normal mixture model exists for the time series data. In addition, a prediction equation is obtained from the analysis.