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1. | linear equation | Yi,new = C 1Yi,old + C2X2,j + C3X3,k + C4X4,l + C5X5,m + C6 |
|---|---|---|
2. | multiplication | Yi = X1,jX2,k |
3. | division | Yi = X1,j / X2,k |
4. | involution | Yi = X1,jX2,k |
5. | natural logarithm | Yi = ln(Xj) |
6. | common logarithm | Yi = 10 log(Xj) |
7. | exponential | Yi = exp(Xj) |
8. | power of 10 | Yi = 10Xj |
9. | power | Yi = XjC1 |
10. | power of constant | Yi = C1Xj |
11. | polynomial | Yi = C0+C1X1,j + C2X1,j2 + C3X1,j3 + C4X1,j4 |
12. | conditional | Yi = max (X1,j, X2,k, X3,l, X4,m, C) |
13. | conditional | Yi = min (X1,j, X2,k, X3,l, X4,m, C) |
14. | conditional | Yi = mean (X1,j, X2,k, X3,l, X4,m ) |
15. | drift | Yi = X1 + C1dt + C0 |
16. | conditional | if Xi < C1 then Yi = C0 else Yi = Xi |
17. | conditional | if Xi > C1 then Yi = C0 else Yi = Xi |
18. | conditional | if Xi < C1 then Yi = C0 else Yi = Xi |
19. | conditional | if Xi = missing then Yi = C0 else Yi = Xi |
20. | conditional | if X1 = missing then Y = X2 else Y = X1 |
where:
Xp = equidistant time series p
Cp = coefficients
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Non-equidistant time series can be transformed into equidistant time series. The function computes the equidistant values in 2 steps. First it aggregates the non-equidistant time steps to equidistant time steps, when calculating the function makes a difference between accumulated parameters and instantaneous parameters. Generally, the non-equidistant series may not fill all equidistant time steps. You can select one of the following options to fill in the gaps:
- zero: the series values at intermediate time steps will be filled with zero's
- missing: the series values at intermediate time steps will be filled with missing values
- linear interpolation: the series values at intermediate time steps will be a linear interpolation between surrounding non-equidistant series observations
- equal to last: the series values at intermediate time steps will be equal to the last observation, (i.e. block-type filling-in).
A special option is "Average over time step". This option uses a weighted average over the values in the next time step. In the previous example, the value for 01-01-2000 05:30 is 0.0033, this is the weighted average for all time steps between 05:00 and 05:30 ((15*-0.015 + 2*0.019 + 5*0.017 + 6*0.026 + 2*0.022)/30 = 0.0033). For filling the gaps the value of the next time step is used.
Example:
The underneath table shows the differences between the 4 options for filling in the gaps.
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