Smoothing Spline Technique For Time Series Data with Autocorrelation

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 ( =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.