Smoothing Spline Technique For Time Series Data with Autocorrelation.

      Аннотация: The study proposes a smoothing method which is the arithmetic weighted value of Generalized Cross-Validation (GCV) and Unbiased Risk (UBR) methods. This study concluded that the PSM method provides the best-fit as a smoothing method, works well at autocorrelation levels (ρ=0.2, 0.5 and 0.8), and does not overfit time-series observations. The study recommended that the proposed smoothing is appropriate for time series observations with autocorrelation in the error term and econometrics real-life data. This study can be applied to: non – parametric regression, non – parametric forecasting, spatial, survival and econometrics observations.

Ключевые слова: Spline Smoothing, Generalized Cross Validation, Generalized Maximum Likelihood, Unbiased Risk, Proposed Smoothing Method, Predictive Mean Square Error