The data is also processed with the consolidation function (I<CF>) of
the archive. There are several consolidation functions that
consolidate primary data points via an aggregate function: B<AVERAGE>,
-B<MIN>, B<MAX>, B<LAST>. The format of B<RRA> line for these
+B<MIN>, B<MAX>, B<LAST>.
+
+=over
+
+=item AVERAGE
+
+the average of the data points is stored.
+
+=item MIN
+
+the smallest of the data points is stored.
+
+=item MAX
+
+the largest of the data points is stored.
+
+=item LAST
+
+the last data points is used.
+
+=back
+
+Note that data aggregation inevitably leads to loss of precision and
+information. The trick is to pick the aggregate function such that the
+I<interesting> properties of your data is kept across the aggregation
+process.
+
+
+The format of B<RRA> line for these
consolidation functions is:
B<RRA:>I<AVERAGE | MIN | MAX | LAST>B<:>I<xff>B<:>I<steps>B<:>I<rows>
=item *
-B<RRA:>I<SEASONAL>B<:>I<seasonal period>B<:>I<gamma>B<:>I<rra-num>
+B<RRA:>I<MHWPREDICT>B<:>I<rows>B<:>I<alpha>B<:>I<beta>B<:>I<seasonal period>[B<:>I<rra-num>]
=item *
-B<RRA:>I<DEVSEASONAL>B<:>I<seasonal period>B<:>I<gamma>B<:>I<rra-num>
+B<RRA:>I<SEASONAL>B<:>I<seasonal period>B<:>I<gamma>B<:>I<rra-num>[B<:smoothing-window=>I<fraction>]
+
+=item *
+
+B<RRA:>I<DEVSEASONAL>B<:>I<seasonal period>B<:>I<gamma>B<:>I<rra-num>[B<:smoothing-window=>I<fraction>]
=item *
These B<RRAs> differ from the true consolidation functions in several ways.
First, each of the B<RRA>s is updated once for every primary data point.
Second, these B<RRAs> are interdependent. To generate real-time confidence
-bounds, a matched set of HWPREDICT, SEASONAL, DEVSEASONAL, and
-DEVPREDICT must exist. Generating smoothed values of the primary data points
-requires both a HWPREDICT B<RRA> and SEASONAL B<RRA>. Aberrant behavior
-detection requires FAILURES, HWPREDICT, DEVSEASONAL, and SEASONAL.
-
-The actual predicted, or smoothed, values are stored in the HWPREDICT
-B<RRA>. The predicted deviations are stored in DEVPREDICT (think a standard
-deviation which can be scaled to yield a confidence band). The FAILURES
-B<RRA> stores binary indicators. A 1 marks the indexed observation as
-failure; that is, the number of confidence bounds violations in the
-preceding window of observations met or exceeded a specified threshold. An
-example of using these B<RRAs> to graph confidence bounds and failures
-appears in L<rrdgraph>.
+bounds, a matched set of SEASONAL, DEVSEASONAL, DEVPREDICT, and either
+HWPREDICT or MHWPREDICT must exist. Generating smoothed values of the primary
+data points requires a SEASONAL B<RRA> and either an HWPREDICT or MHWPREDICT
+B<RRA>. Aberrant behavior detection requires FAILURES, DEVSEASONAL, SEASONAL,
+and either HWPREDICT or MHWPREDICT.
+
+The predicted, or smoothed, values are stored in the HWPREDICT or MHWPREDICT
+B<RRA>. HWPREDICT and MHWPREDICT are actually two variations on the
+Holt-Winters method. They are interchangeable. Both attempt to decompose data
+into three components: a baseline, a trend, and a seasonal coefficient.
+HWPREDICT adds its seasonal coefficient to the baseline to form a prediction, whereas
+MHWPREDICT multiplies its seasonal coefficient by the baseline to form a
+prediction. The difference is noticeable when the baseline changes
+significantly in the course of a season; HWPREDICT will predict the seasonality
+to stay constant as the baseline changes, but MHWPREDICT will predict the
+seasonality to grow or shrink in proportion to the baseline. The proper choice
+of method depends on the thing being modeled. For simplicity, the rest of this
+discussion will refer to HWPREDICT, but MHWPREDICT may be substituted in its
+place.
+
+The predicted deviations are stored in DEVPREDICT (think a standard deviation
+which can be scaled to yield a confidence band). The FAILURES B<RRA> stores
+binary indicators. A 1 marks the indexed observation as failure; that is, the
+number of confidence bounds violations in the preceding window of observations
+met or exceeded a specified threshold. An example of using these B<RRAs> to graph
+confidence bounds and failures appears in L<rrdgraph>.
The SEASONAL and DEVSEASONAL B<RRAs> store the seasonal coefficients for the
Holt-Winters forecasting algorithm and the seasonal deviations, respectively.
be the same for both. Note that I<gamma> can also be changed via the
B<RRDtool> I<tune> command.
+I<smoothing-window> specifies the fraction of a season that should be
+averaged around each point. By default, the value of I<smoothing-window> is
+0.05, which means each value in SEASONAL and DEVSEASONAL will be occasionally
+replaced by averaging it with its (I<seasonal period>*0.05) nearest neighbors.
+Setting I<smoothing-window> to zero will disable the running-average smoother
+altogether.
+
I<rra-num> provides the links between related B<RRAs>. If HWPREDICT is
specified alone and the other B<RRAs> are created implicitly, then
there is no need to worry about this argument. If B<RRAs> are created
projects / rrdtool.git / blobdiff