Fourier Series Nonparametric Regression Modeling in the Case of Rainfall in West Java Province

Abstract

The Fourier series is a trigonometric polynomial that has flexibility, so it adapts effectively to the local nature of the data. This Fourier series estimator is generally used when the data used is investigated for unknown patterns and there is a tendency for seasonal patterns. This study aims to determine the results of the best Fourier series nonparametric regression model and the level of accuracy of the Fourier series nonparametric regression model on rainfall data by month in West Java Province in 2015-2019. This research is about a nonparametric regression model of Fourier series which is estimated using Ordinary Least Square method. Nonparametric regression using the Fourier series approach was applied to Rainfall data in West Java Province in 2015-2019. The independent variables used were the average air humidity, air pressure, wind speed, and air temperature. The model used to model the amount of rainfall in West Java Province is a nonparametric Fourier series. The nonparametric regression model is the best Fourier series with K =13 values obtained Generalized Cross Validation, Mean Square Error, and R2 respectively at 549.92; 462.09; and 97.30%. The results showed that the variables of air humidity and air pressure had a significant effect on rainfall.