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operation ignores all NAN-values in a sliding window and computes the
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average of the remaining values.
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B<PREDICT, PREDICTSIGMA>
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Create a "sliding window" average/sigma of another data series, that also
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shifts the data series by given amounts of of time as well
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Usage - explicit stating shifts:
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CDEF:predict=<shift n>,...,<shift 1>,n,<window>,x,PREDICT
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CDEF:sigma=<shift n>,...,<shift 1>,n,<window>,x,PREDICTSIGMA
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Usage - shifts defined as a base shift and a number of time this is applied
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CDEF:predict=<shift multiplier>,-n,<window>,x,PREDICT
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CDEF:sigma=<shift multiplier>,-n,<window>,x,PREDICTSIGMA
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CDEF:predict=172800,86400,2,1800,x,PREDICT
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This will create a half-hour (1800 second) sliding window average/sigma of x, that
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average is essentially computed as shown here:
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+---!---!---!---!---!---!---!---!---!---!---!---!---!---!---!---!---!--->
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<----------------------->
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<----------------------------------------------->
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<----------------------->
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<----------------------------------------------->
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Value at sample (t0) will be the average between (t0-shift1-window) and (t0-shift1)
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and between (t0-shift2-window) and (t0-shift2)
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Value at sample (t1) will be the average between (t1-shift1-window) and (t1-shift1)
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and between (t1-shift2-window) and (t1-shift2)
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The function is by design NAN-safe.
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This also allows for extrapolation into the future (say a few days)
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- you may need to define the data series whit the optional start= parameter, so that
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the source data series has enough data to provide prediction also at the beginning of a graph...
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Here an example, that will create a 10 day graph that also shows the
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prediction 3 days into the future with its uncertainty value (as defined by avg+-4*sigma)
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This also shows if the prediction is exceeded at a certain point.
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rrdtool graph image.png --imgformat=PNG \
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--start=-7days --end=+3days --width=1000 --height=200 --alt-autoscale-max \
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DEF:value=value.rrd:value:AVERAGE:start=-14days \
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LINE1:value#ff0000:value \
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CDEF:predict=86400,-7,1800,value,PREDICT \
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CDEF:sigma=86400,-7,1800,value,PREDICTSIGMA \
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CDEF:upper=predict,sigma,3,*,+ \
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CDEF:lower=predict,sigma,3,*,- \
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LINE1:predict#00ff00:prediction \
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LINE1:upper#0000ff:upper\ certainty\ limit \
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LINE1:lower#0000ff:lower\ certainty\ limit \
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CDEF:exceeds=value,UN,0,value,lower,upper,LIMIT,UN,IF \
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TICK:exceeds#aa000080:1
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Note: Experience has shown that a factor between 3 and 5 to scale sigma is a good
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discriminator to detect abnormal behavior. This obviously depends also on the type
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of data and how "noisy" the data series is.
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This prediction can only be used for short term extrapolations - say a few days into the future-
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=item Special values
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Returns the rate from each defined time slot multiplied with the
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step size. This can, for instance, return total bytes transfered
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step size. This can, for instance, return total bytes transferred
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when you have logged bytes per second. The time component returns
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the number of seconds.
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Example: C<VDEF:total=mydata,TOTAL>
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=item PERCENT, PERCENTNAN
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This should follow a B<DEF> or B<CDEF> I<vname>. The I<vname> is popped,
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another number is popped which is a certain percentage (0..100). The
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data set is then sorted and the value returned is chosen such that
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I<percentage> percent of the values is lower or equal than the result.
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For PERCENTNAN I<Unknown> values are ignored, but for PERCENT
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I<Unknown> values are considered lower than any finite number for this
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purpose so if this operator returns an I<unknown> you have quite a lot
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of them in your data. B<Inf>inite numbers are lesser, or more, than the
added
removed
doc/rrdgraph_rpn.pod
