Smoothing Spline Technique For Time Series Data with Autocorrelation

By Samuel Olorunfemi Adams

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.

• Format: Paperback
• Language: English
• ISBN: 6206151891
• ISBN13: 9786206151890
• Release Date: March 2023
• Publisher: LAP Lambert Academic Publishing
• Length: 144 Pages
• Weight: 0.49 lbs.
• Dimensions: 0.3" x 6.0" x 9.0"