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source value is \s-1NAN\s0 the complete sliding window is affected. The \s-1TRENDNAN\s0
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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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\&\fB\s-1PREDICT\s0, \s-1PREDICTSIGMA\s0\fR
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Create a \*(L"sliding window\*(R" 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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\& <\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\->
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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 \e
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\-\-start=\-7days \-\-end=+3days \-\-width=1000 \-\-height=200 \-\-alt\-autoscale\-max \e
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DEF:value=value.rrd:value:AVERAGE:start=\-14days \e
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LINE1:value#ff0000:value \e
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CDEF:predict=86400,\-7,1800,value,PREDICT \e
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CDEF:sigma=86400,\-7,1800,value,PREDICTSIGMA \e
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CDEF:upper=predict,sigma,3,*,+ \e
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CDEF:lower=predict,sigma,3,*,\- \e
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LINE1:predict#00ff00:prediction \e
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LINE1:upper#0000ff:upper\e certainty\e limit \e
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LINE1:lower#0000ff:lower\e certainty\e limit \e
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CDEF:exceeds=value,UN,0,value,lower,upper,LIMIT,UN,IF \e
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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 \*(L"noisy\*(R" 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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.IP "Special values" 4
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.IX Item "Special values"
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\&\fB\s-1UNKN\s0\fR
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.IP "\s-1TOTAL\s0" 4
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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: \f(CW\*(C`VDEF:total=mydata,TOTAL\*(C'\fR
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.IP "\s-1PERCENT\s0" 4
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.IP "\s-1PERCENT\s0, \s-1PERCENTNAN\s0" 4
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.IX Item "PERCENT, PERCENTNAN"
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This should follow a \fB\s-1DEF\s0\fR or \fB\s-1CDEF\s0\fR \fIvname\fR. The \fIvname\fR 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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\&\fIpercentage\fR percent of the values is lower or equal than the result.
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For \s-1PERCENTNAN\s0 \fIUnknown\fR values are ignored, but for \s-1PERCENT\s0
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\&\fIUnknown\fR values are considered lower than any finite number for this
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purpose so if this operator returns an \fIunknown\fR you have quite a lot
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of them in your data. \fBInf\fRinite numbers are lesser, or more, than the
added
removed
doc/rrdgraph_rpn.1
