| Stochastic Drift |
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STOCHASTIC DRIFTS Longitudinal Studies of secular events are frequently conceptualized as consisting of a trend component fitted by a Polynomial , a cyclical component often fitted by an analysis based on Autocorrelation s or on a Fourier Series , and a random component (stochastic drift) to be removed. In the course of the Time Series Analysis , identification of cyclical and stochastic drift components is often attempted by alternating autocorrelation analysis and differencing of the trend. Autocorrelation analysis helps to identify the correct phase of the fitted model while the successive differencing transforms the stochastic drift component into White Noise . REFERENCES
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