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Other smoothing methods, like HANTS, are well-known for time-series smoothing; however, HANTS tends to ensure spatial completeness but sacrifices important information in the time dimension [34]; meanwhile such drowned information is important in delineating vegetation growth.
Time-series smoothing must be done to retrieve the essential shape of a curve.
When performing time-series smoothing, researchers are also reminded that maintenance of the original characteristics of the time-series profile is critical [25].
She has numerous publications, many of which are on forecasting models for seasonal time series and exponential smoothing methods.
For time series, the usual smoothing method that has been used for producing residuals with no seasonality was locally weighted regression smoothers (LOESS) within the generalized additive models (GAMs) (Hastie and Tibshirani 1990).
A variety of time series prediction methods are available such as the Moving Averages, Linear Regression and Exponential Smoothing [14 16].
While there are frequently general trends in immunization coverage levels, no attempt is made to fit data points with smoothing techniques or time series methods though the estimation process does allow for interpolation within the time series and extrapolation at the end of the time series.
However, the existing time series modeling methods may not meet the requirements of time series stationary.
Here we choose some typical time series forecasting methods.
The uncertainties are predicted by time series methods.
The team used time series methods for data analysis.
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