BibTex Citation Data :
@article{J.Gauss9484, author = {Novia Agustina and Suparti Suparti and Moch. Mukid}, title = {PEMODELAN DATA INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN REGRESI PENALIZED SPLINE}, journal = {Jurnal Gaussian}, volume = {4}, number = {3}, year = {2015}, keywords = {Indonesia Composite Index, Nonparametric Regression, Penalized Spline Regression, GCV, MAPE}, abstract = { Indonesia Composite Index (IHSG) is an indicator of stock price changes in Indonesia Stock Exchange. IHSG is time series data that can be modeled with parametric models. But there are some assumptions for parametric model, while the fluctuated IHSG data usually doesn’t occupy these assumptions. Another alternative for this study is nonparametric regression. Penalized spline regression is one of nonparametric regression method that can be used. The optimal penalized spline models depends on the determination of the optimal smoothing parameter λ and the optimal number of knots, that has a minimum value of Generalized Cross Validation (GCV). The best model in this study is penalized spline degree 1 (linear) with 1 knot, that is 5120,625, smoothing parameter λ value is 41590, and GCV value is 1567,203. R 2 value for in sample data is 83,2694% and R 2 value for out sample data is 96,4976% show that the model have a very good performance. MAPE values for in sample data is 0,5983% and MAPE values for out sample data is 0,4974%. Because the value of MAPE in sample and out sample is less than 10%, it means that the performance of the model and forecasting are very accurate. Keywords: Indonesia Composite Index, Nonparametric Regression, Penalized Spline Regression, GCV, MAPE }, issn = {2339-2541}, pages = {603--612} doi = {10.14710/j.gauss.4.3.603-612}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/9484} }
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Indonesia Composite Index (IHSG) is an indicator of stock price changes in Indonesia Stock Exchange. IHSG is time series data that can be modeled with parametric models. But there are some assumptions for parametric model, while the fluctuated IHSG data usually doesn’t occupy these assumptions. Another alternative for this study is nonparametric regression. Penalized spline regression is one of nonparametric regression method that can be used. The optimal penalized spline models depends on the determination of the optimal smoothing parameter λ and the optimal number of knots, that has a minimum value of Generalized Cross Validation (GCV). The best model in this study is penalized spline degree 1 (linear) with 1 knot, that is 5120,625, smoothing parameter λ value is 41590, and GCV value is 1567,203. R2 value for in sample data is 83,2694% and R2 value for out sample data is 96,4976% show that the model have a very good performance. MAPE values for in sample data is 0,5983% and MAPE values for out sample data is 0,4974%. Because the value of MAPE in sample and out sample is less than 10%, it means that the performance of the model and forecasting are very accurate.
Keywords: Indonesia Composite Index, Nonparametric Regression, Penalized Spline Regression, GCV, MAPE
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