In the era of Big Data, statistical modelling plays important role in handling a prodigious flow of datasets. The existing literatures regarding the performance of maximum likelihood estimation and Bayesian method that fit with finite mixture model in time series modelling is still lacking. The main objective of this study was to compare the maximum likelihood estimation and Bayesian method in fitting with finite mixture model and determine the plausible method in analysing time series data. Also, this study aimed to identify the number of components and the representation existed in time series data. Additionally, this study also evaluated and modelled the exchange rate, inflation rate, electrical and electronic export values in Malaysia, Thailand and the Philippines using both methods that fit to finite mixture model. The finite mixture model is an unsupervised learning model that can fit with all types of distributions and hence modelling a variety of data. In this study,.....

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