ANALYSIS OF KERNEL-BASED COPULA PROCESSES MODEL AND ITS APPLICATIONS - Shabrina, Ayu
Prediction of time series data is an effort to determine the value of the data in the future by using historical data. The results of this prediction is very useful both in financial data or non - financial data. Distribution function prediction can be obtained using Bayesian concept of joint distribution function. Construction of joint distribution function from some specific distributed random variables is certainly not easy to do. So we propose to use Copula function to build the joint distribution function. Kernel - based Copula Processes (KCP) is a process that uses the kernel as component parameter of copula Elliptical. This kernel acts as a value of the correlation or dependence of data that has certain characteristics. This thesis studied further about KCP models to determine the distribution of predictions using univariate and multivariate analysis. The distribution of predictions can be used to predict the time series data for the next period of time.