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The correlation analysis made between displacement and piezometric time series provides a correlation coefficient greater than 85% for all the wells.
First, TFA of longer time series provides more representative measures of seasonality and allows assessment of change over time.
Examination of the glucose and insulin data from the time series provides a clear explanation for the difference.
These results suggest that EF measured early in the dynamic time series provides different physiological information from AH and rSI.
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Streamflow time series provide baseline data for many hydrological investigations.
CHAMP vector and Ørsted scalar data dominate early in the time series, providing good global coverage.
We used the estimated GPS ground motion time series provided by GPS Solutions.
The time series provide the input for statistical methods for signal generation, CUSUM and Farrington.
These monocomponent signals, named intrinsic mode functions (IMFs), can be directly extracted from the original time series, provided that they satisfy two important conditions.
Static, saturated areas (e.g., urban centers) are automatically filtered with a resultant time series providing a dynamic characterization of detected changes.
We compared our monthly coefficient estimates of degree two with SLR and Gravity Recovery and Climate Experiment (GRACE) time series provided by the Center for Space Research CSRR) at Austin, Texas.
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