FOURIER SERIES APPLICATION FOR MODELING “CHOCOLATE” KEYWORD SEARCH TRENDS IN GOOGLE TRENDS DATA

Andrea Tri Rian Dani (Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, Indonesia)
Fachrian Bimantoro Putra (Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, Indonesia)
Muhammad Aldani Zen (Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, Indonesia)
Vita Ratnasari (Department of Statistics, Faculty of Science and Data Analytics, Sepuluh Nopember Institute of Technology, Surabaya, Indonesia)
Qonita Qurrota A'yun (Mathematics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, Indonesia)

Jurnal Statistika Universitas Muhammadiyah Semarang
Vol 11, No 1 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang

In some cases of regression modeling, it is very common to find a repeating pattern. To model this, of course, the approach used must be in accordance with the characteristics of the data. The Fourier series is one of the proposed approaches, because it has advantages in modeling relationship patterns that tend to repeat, such as cosine sine waves. The Fourier series is a subset of nonparametric regression, which has good flexibility in modeling. In this study, the Fourier series approach was applied to model search trend data for the keyword "Chocolate" sourced from Google Trends. Generalized Cross-Validation (GCV) is used as model evaluation criteria. Based on the results of the analysis, the best Fourier series nonparametric regression model is obtained with the number of oscillations of five, which is indicated by the minimum GCV value.