Enhancing the Prediction Accuracy of the Length of Day Change by Eliminating the Edge-effect of Least Squares Fitting

In order to eliminate the edge-effect of least squares (LS) fitting for the length of day change (ΔLOD), the time-series analysis model is first used to extrapolate ΔLOD series forward and backward and then generate a new series. Subsequently, the cofficients of a LS model are estimated using the new generated series. As a result, the edge-effect is changed to the edge of the new series, and thus the original fitted ΔLOD series can be free from the edge-effect. Finally, a combination of LS and autoregressive (AR) models (LS+AR) is employed to predict the original ΔLOD data. The results indicate that the proposed method can efficiently eliminate the edge-effect, and thus improve the prediction accurcy of the LS+AR model, especially for medium- and long-term prediction.