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diff --git a/doc/rrdgraph_rpn.pod b/doc/rrdgraph_rpn.pod
index 5826c802905f04ead7cc9ca146b053335674c5e5..894fef16bbabcf42215946a1f880d8241d954f38 100644 (file)
--- a/doc/rrdgraph_rpn.pod
+++ b/doc/rrdgraph_rpn.pod
Pop two elements from the stack, compare them for the selected condition
and return 1 for true or 0 for false. Comparing an I<unknown> or an
-I<infinite> value will always result in 0 (false).
+I<infinite> value will result in I<unknown> returned ... which will also be
+treated as false by the B<IF> call.
B<UN, ISINF>
operation ignores all NAN-values in a sliding window and computes the
average of the remaining values.
+B<PREDICT, PREDICTSIGMA>
+
+Create a "sliding window" average/sigma of another data series, that also
+shifts the data series by given amounts of of time as well
+
+Usage - explicit stating shifts:
+CDEF:predict=<shift n>,...,<shift 1>,n,<window>,x,PREDICT
+CDEF:sigma=<shift n>,...,<shift 1>,n,<window>,x,PREDICTSIGMA
+
+Usage - shifts defined as a base shift and a number of time this is applied
+CDEF:predict=<shift multiplier>,-n,<window>,x,PREDICT
+CDEF:sigma=<shift multiplier>,-n,<window>,x,PREDICTSIGMA
+
+Example:
+CDEF:predict=172800,86400,2,1800,x,PREDICT
+
+This will create a half-hour (1800 second) sliding window average/sigma of x, that
+average is essentially computed as shown here:
+
+ +---!---!---!---!---!---!---!---!---!---!---!---!---!---!---!---!---!--->
+ now
+ shift 1 t0
+ <----------------------->
+ window
+ <--------------->
+ shift 2
+ <----------------------------------------------->
+ window
+ <--------------->
+ shift 1 t1
+ <----------------------->
+ window
+ <--------------->
+ shift 2
+ <----------------------------------------------->
+ window
+ <--------------->
+
+ Value at sample (t0) will be the average between (t0-shift1-window) and (t0-shift1)
+ and between (t0-shift2-window) and (t0-shift2)
+ Value at sample (t1) will be the average between (t1-shift1-window) and (t1-shift1)
+ and between (t1-shift2-window) and (t1-shift2)
+
+
+The function is by design NAN-safe.
+This also allows for extrapolation into the future (say a few days)
+- you may need to define the data series whit the optional start= parameter, so that
+the source data series has enough data to provide prediction also at the beginning of a graph...
+
+Here an example, that will create a 10 day graph that also shows the
+prediction 3 days into the future with its uncertainty value (as defined by avg+-4*sigma)
+This also shows if the prediction is exceeded at a certain point.
+
+rrdtool graph image.png --imgformat=PNG \
+ --start=-7days --end=+3days --width=1000 --height=200 --alt-autoscale-max \
+ DEF:value=value.rrd:value:AVERAGE:start=-14days \
+ LINE1:value#ff0000:value \
+ CDEF:predict=86400,-7,1800,value,PREDICT \
+ CDEF:sigma=86400,-7,1800,value,PREDICTSIGMA \
+ CDEF:upper=predict,sigma,3,*,+ \
+ CDEF:lower=predict,sigma,3,*,- \
+ LINE1:predict#00ff00:prediction \
+ LINE1:upper#0000ff:upper\ certainty\ limit \
+ LINE1:lower#0000ff:lower\ certainty\ limit \
+ CDEF:exceeds=value,UN,0,value,lower,upper,LIMIT,UN,IF \
+ TICK:exceeds#aa000080:1
+
+Note: Experience has shown that a factor between 3 and 5 to scale sigma is a good
+discriminator to detect abnormal behavior. This obviously depends also on the type
+of data and how "noisy" the data series is.
+
+This prediction can only be used for short term extrapolations - say a few days into the future-
=item Special values
=item LAST, FIRST
-Return the last/first value including its time. The time for
-FIRST is actually the start of the corresponding interval, whereas
-LAST returns the end of the corresponding interval.
+Return the last/first non-nan or infinite value for the selected data
+stream, including its timestamp.
Example: C<VDEF:first=mydata,FIRST>
=item TOTAL
Returns the rate from each defined time slot multiplied with the
-step size. This can, for instance, return total bytes transfered
+step size. This can, for instance, return total bytes transferred
when you have logged bytes per second. The time component returns
the number of seconds.
Example: C<VDEF:total=mydata,TOTAL>
-=item PERCENT
+=item PERCENT, PERCENTNAN
This should follow a B<DEF> or B<CDEF> I<vname>. The I<vname> is popped,
another number is popped which is a certain percentage (0..100). The
data set is then sorted and the value returned is chosen such that
I<percentage> percent of the values is lower or equal than the result.
+For PERCENTNAN I<Unknown> values are ignored, but for PERCENT
I<Unknown> values are considered lower than any finite number for this
purpose so if this operator returns an I<unknown> you have quite a lot
of them in your data. B<Inf>inite numbers are lesser, or more, than the
(NaN E<lt> -INF E<lt> finite values E<lt> INF)
Example: C<VDEF:perc95=mydata,95,PERCENT>
+ C<VDEF:percnan95=mydata,95,PERCENTNAN>
=item LSLSLOPE, LSLINT, LSLCORREL