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docs: update function docs on deriv
Signed-off-by: mtfoley <mtfoley.mae@gmail.com>
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@ -130,6 +130,11 @@ delta(cpu_temp_celsius{host="zeus"}[2h])
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`deriv(v range-vector)` calculates the per-second derivative of the time series in a range
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vector `v`, using [simple linear regression](https://en.wikipedia.org/wiki/Simple_linear_regression).
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The range vector must have at least two samples in order to perform the calculation. The linear
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regression is used to calculate a slope and offset for a trend line, and the slope value is used
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for the derivative calculation. In order to reduce calculation errors, the timestamp of the first
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sample in the range vector is set as `x=0` within the linear regression algorithm. When `+Inf` or
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`-Inf` are found in the range vector, the slope and offset value calculated will be `NaN`.
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`deriv` should only be used with gauges.
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@ -329,6 +334,13 @@ January etc.
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`predict_linear(v range-vector, t scalar)` predicts the value of time series
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`t` seconds from now, based on the range vector `v`, using [simple linear
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regression](https://en.wikipedia.org/wiki/Simple_linear_regression).
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The range vector must have at least two samples in order to perform the
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calculation. The linear regression is used to calculate a slope and offset
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for a trend line, and the slope and offset are used to calculate the predict
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the value of the time series. In order to reduce calculation errors, the
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timestamp of the first sample in the range vector is set as `x=0` within the
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linear regression algorithm. When `+Inf` or `-Inf` are found in the range vector,
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the slope and offset value calculated will be `NaN`.
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`predict_linear` should only be used with gauges.
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@ -456,4 +468,4 @@ The following are useful for converting between degrees and radians:
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- `deg(v instant-vector)`: converts radians to degrees for all elements in `v`.
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- `pi()`: returns pi.
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- `rad(v instant-vector)`: converts degrees to radians for all elements in `v`.
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- `rad(v instant-vector)`: converts degrees to radians for all elements in `v`.
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