Pemodelan Data Time Series dengan Pendekatan Regresi Nonparametrik B-Spline

Zulaiha Rahasia, Resmawan Resmawan, Dewi Rahmawaty Isa

Abstract


Spline is one of the nonparametric approach, to adjust data so the final model has good flexibility. The purpose of this research is to model the time series data in the form of currency exchange rates by using the nonparametric B-spline approach. In B-spline modelling, determination of the order for the model, and the number and the placement of the knot are the criteria that must be considered. The best B-spline model obtained based on the selection of the optimal knot points with minimum Generalized Cross Validation (GCV) criteria. The modelling in this research use data on the exchange rate of the rupiah toward the US dollar in the period January 2014 - December 2018. The best B-spline model obtained by the 2 point knot approach, at points 11935.10 and 12438.29, with GCV valueequals to 55683.09.

Keywords: Nonparametric Regression; B-Spline; Generalized Cross Validation


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References


Devi, A. R., Mukid, M. A., & Yasin, H. (2014). Analisis Inflasi Kota Semarang Menggunakan Metode Regresi Nonparametrik B-Spline. Jurnal Gaussian, 3(2), 193–202.

Ekananda, M. (2014). Analisis Data Time Series. Jakarta: Mitra Wacana Media.

Ekananda, M. (2015). Ekonometrika Dasar. Jakarta: Mitra Wacana Media.

Eubank, R. L. (1999). Nonparametric Regression and Spline Smoothing (2nd ed.). New York: Marcel Dekker Inc.

Hardle, W. (1990). Applied Nonparametric Regression. New York: Cambridge University Press.

Katijaya, S. S., Suparti, S., & Sudarno, S. (2013). Regresi Spline sebagai Alternatif dalam Pemodelan Kurs Rupiah terhadap Dollar Amerika Serikat. Jurnal Gaussian, 2(3), 229–238.

Kholid, N., & Achmadi, A. (2008). Metodologi Penelitian. Jakarta: Bumi Aksara.

Makridakis, S., Wheelwright, C., & McGee, V. E. (1998). Forecasting Method and Applications (2nd ed.). New York: John Wiley & Sons Inc.

Ndangi, W. R. A., Resmawan, R., & Djakaria, I. (2019). Perbandingan Analisis Diskriminan dan Regresi Logistik Multinomial. Jambura Journal of Mathematics, 1(2), 54–63. https://doi.org/10.34312/jjom.v1i2.2100

Ningsih, S., & Dukalang, H. H. (2019). Penerapan Metode Suksesif Interval pada Analsis Regresi Linier Berganda. Jambura Journal of Mathematics, 1(1), 43–53. https://doi.org/10.34312/jjom.v1i1.1742

Suparti, S., Santoso, R., Prahutama, A., & Devi, A. R. (2018). Regresi Nonparametrik. Ponorogo: Wade Group.

Wahba, G. (1990). Spline models for observational data. Pennsylvania: SIAM.




DOI: https://doi.org/10.26877/aks.v11i1.4903

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