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A relation curve gives a functional relationship between two series of the following form Yt = F(Xt+t1 ). The curves can be used for:
1. Detection of random errors,
2. Detection of systematic errors,
3. Filling in of missing data, and
4. Forecasting purposes.
If there is a distinct one to one relationship between two series random errors will be shown in a relation curve plot as outliers. To arrive at a one to one relationship (i.e. elimination of looping) the introduction of a time shift (t1) between the two series may be necessary.
By comparing two relation curves or data of one period with the curve of another period, shifts in relationships, e.g. in water level series due to changes in the gauge zero, can be detected.
The relation curve fitted to the data of two series can be used to fill-in missing data in the dependent variable of the relation (Y), see Section Interpolation.
Relation curves{^}image001.gif!
If the series in the relation are mutually shifted in time, with sufficient lead-time for the independent variable X (t1 negative), the relation curve may be used to forecast the dependent variable in the relation Y from observations on X.
The parameters of the established relationships for a period of time can be stored in the data base for e.g. later comparison, filling-in missing data.
The main options under <Relation curves> include:
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