The function may be called by the names: g13auc, nag_tsa_uni_means or nag_tsa_mean_range.
Let denote successive observations in a time series. The series may be divided into groups of successive values and for each group the range or standard deviation (depending on a user-supplied option) and the mean are calculated. If is not a multiple of then groups of equal size are found starting from the end of the series of observations provided, and any remaining observations at the start of the series are ignored. The number of groups used, , is the integer part of . If you wish to ensure that no observations are ignored then the number of observations, , should be chosen so that is divisible by .
The mean, , the range, , and the standard deviation, , for the th group are defined as
where , the number of observations ignored.
For seasonal data it is recommended that should be equal to the seasonal period. For non-seasonal data the recommended group size is .
A plot of range against mean or of standard deviation against mean is useful for finding a transformation of the series which makes the variance constant. If the plot appears random or the range (or standard deviation) seems to be constant irrespective of the mean level then this suggests that no transformation of the time series is called for. On the other hand an approximate linear relationship between range (or standard deviation) and mean would indicate that a log transformation is appropriate. Further details may be found in either Jenkins (1979) or McLeod (1982).
You have the choice of whether to use the range or the standard deviation as a measure of variability. If the group size is small they are both equally good but if the group size is fairly large (e.g., for monthly data) then the range may not be as good an estimate of variability as the standard deviation.
Jenkins G M (1979) Practical Experiences with Modelling and Forecasting Time Series GJP Publications, Lancaster
McLeod G (1982) Box–Jenkins in Practice. 1: Univariate Stochastic and Single Output Transfer Function/Noise Analysis GJP Publications, Lancaster
1: – IntegerInput
On entry: , the number of observations in the time series.
2: – const doubleInput
On entry: must contain the th observation , for .
3: – IntegerInput
On entry: , the group size.
4: – Nag_RangeStatInput
On entry: indicates whether ranges or standard deviations are to be calculated.
Ranges are calculated.
Standard deviations are calculated.
5: – doubleOutput
On exit: contains the range or standard deviation, as determined by rs, of the th group of observations, for .
6: – doubleOutput
On exit: contains the mean of the th group of observations, for .
7: – NagError *Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).
6Error Indicators and Warnings
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
On entry, argument had an illegal value.
On entry, .
On entry, and .
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
The computations are believed to be stable.
8Parallelism and Performance
g13auc is not threaded in any implementation.
The time taken by g13auc is approximately proportional to .
The following program produces the statistics for a range-mean plot for a series of observations divided into groups of .