FUNCTIONAL PREDICTION REGRESSION VIA BOOSTING UNTUK PEMODELAN CURAH HUJAN DI PROVINSI JAWA TIMUR - Khusnia Nurul Khikmah (MATHunesa: Jurnal Ilmiah Matematika, 2021)
Natural phenomena that can cause natural changes on earth caused by increasing greenhouse gases and decreasing landthat absorbs carbon dioxide are called climate change. The elements that cause changes include rainfall and temperature.The constantly rising temperature of the earth results in changing rainfall patterns and can have various effects on theenvironment. Therefore, research on rainfall modeling with annual average temperature and rainfall data from theprovince of East Java from 2006 to 2017 which was taken from the official website of the Central Statistics Agency ofEast Java Province makes sense. This data is a data multivariate time series that is approached with Functional DataAnalysis and modeled using Functional Prediction Regression. Functional Prediction Regression is a form of modelingwith functional data that can test the overall model for high-dimensional data and one of the improved methods ofregression methods for functional data. One way to model Functional Prediction Regression is through boosting. Thisresearch conducted rainfall modeling with Functional Prediction Regression through boosting from East Java Provinceand obtained modeling results using an additional predictor model with 5-fold bootstrap and adjustment of the splineregression (knots) used, namely 16 indicated by the iteration value-boosting bootstrap where the model used to have alinear functional effect of ???????????????????? for the mu parameter and 100 for the sigma parameter.