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 behaviour. 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
add_op(OP_REV, REV)
add_op(OP_TREND, TREND)
add_op(OP_TRENDNAN, TRENDNAN)
+ add_op(OP_PREDICT, PREDICT)
+ add_op(OP_PREDICTSIGMA, PREDICTSIGMA)
add_op(OP_RAD2DEG, RAD2DEG)
add_op(OP_DEG2RAD, DEG2RAD)
add_op(OP_AVG, AVG)
match_op(OP_REV, REV)
match_op(OP_TREND, TREND)
match_op(OP_TRENDNAN, TRENDNAN)
+ match_op(OP_PREDICT, PREDICT)
+ match_op(OP_PREDICTSIGMA, PREDICTSIGMA)
match_op(OP_RAD2DEG, RAD2DEG)
match_op(OP_DEG2RAD, DEG2RAD)
match_op(OP_AVG, AVG)
}
}
break;
+ case OP_PREDICT:
+ case OP_PREDICTSIGMA:
+ stackunderflow(2);
+ {
+ /* the local averaging window (similar to trend, but better here, as we get better statistics thru numbers)*/
+ int locstepsize = rpnstack->s[--stptr];
+ /* the number of shifts and range-checking*/
+ int shifts = rpnstack->s[--stptr];
+ stackunderflow(shifts);
+ // handle negative shifts special
+ if (shifts<0) {
+ stptr--;
+ } else {
+ stptr-=shifts;
+ }
+ /* the real calculation */
+ double val=DNAN;
+ /* the info on the datasource */
+ time_t dsstep = (time_t) rpnp[rpi - 1].step;
+ int dscount = rpnp[rpi - 1].ds_cnt;
+ int locstep = (int)ceil((float)locstepsize/(float)dsstep);
+
+ /* the sums */
+ double sum = 0;
+ double sum2 = 0;
+ int count = 0;
+ /* now loop for each position */
+ int doshifts=shifts;
+ if (shifts<0) { doshifts=-shifts; }
+ for(int loop=0;loop<doshifts;loop++) {
+ /* calculate shift step */
+ int shiftstep=1;
+ if (shifts<0) {
+ shiftstep = loop*rpnstack->s[stptr];
+ } else {
+ shiftstep = rpnstack->s[stptr+loop];
+ }
+ if(shiftstep <0) {
+ rrd_set_error("negative shift step not allowed: %i",shiftstep);
+ return -1;
+ }
+ shiftstep=(int)ceil((float)shiftstep/(float)dsstep);
+ /* loop all local shifts */
+ for(int i=0;i<=locstep;i++) {
+ /* now calculate offset into data-array - relative to output_idx*/
+ int offset=shiftstep+i;
+ /* and process if we have index 0 of above */
+ if ((offset>=0)&&(offset<output_idx)) {
+ /* get the value */
+ val =rpnp[rpi - 1].data[-dscount * offset];
+ /* and handle the non NAN case only*/
+ if (! isnan(val)) {
+ sum+=val;
+ sum2+=val*val;
+ count++;
+ }
+ }
+ }
+ }
+ /* do the final calculations */
+ val=DNAN;
+ if (rpnp[rpi].op == OP_PREDICT) { /* the average */
+ if (count>0) {
+ val = sum/(double)count;
+ }
+ } else {
+ if (count>1) { /* the sigma case */
+ val=count*sum2-sum*sum;
+ if (val<0) {
+ val=DNAN;
+ } else {
+ val=sqrt(val/((float)count*((float)count-1.0)));
+ }
+ }
+ }
+ rpnstack->s[stptr] = val;
+ }
+ break;
case OP_TREND:
case OP_TRENDNAN:
stackunderflow(1);
projects / rrdtool.git / commitdiff