NAG CL Interface
g05pmc (times_​smooth_​exp)

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1 Purpose

g05pmc simulates from an exponential smoothing model, where the model uses either single exponential, double exponential or a Holt–Winters method.

2 Specification

#include <nag.h>
void  g05pmc (Nag_InitialValues mode, Integer n, Nag_ExpSmoothType itype, Integer p, const double param[], const double init[], double var, double r[], Integer state[], const double e[], Integer en, double x[], NagError *fail)
The function may be called by the names: g05pmc, nag_rand_times_smooth_exp or nag_rand_exp_smooth.

3 Description

g05pmc returns {xt:t=1,2,,n}, a realization of a time series from an exponential smoothing model defined by one of five smoothing functions: where mt is the mean, rt is the trend and st is the seasonal component at time t with p being the seasonal order. The errors, εt are either drawn from a normal distribution with mean zero and variance σ2 or randomly sampled, with replacement, from a user-supplied vector.

4 References

Chatfield C (1980) The Analysis of Time Series Chapman and Hall

5 Arguments

1: mode Nag_InitialValues Input
On entry: indicates if g05pmc is continuing from a previous call or, if not, how the initial values are computed.
mode=Nag_InitialValuesSupplied
Values for m0, r0 and s-j, for j=0,1,,p-1, are supplied in init.
mode=Nag_ContinueNoUpdate
g05pmc continues from a previous call using values that are supplied in r. r is not updated.
mode=Nag_ContinueAndUpdate
g05pmc continues from a previous call using values that are supplied in r. r is updated.
Constraint: mode=Nag_InitialValuesSupplied, Nag_ContinueNoUpdate or Nag_ContinueAndUpdate.
2: n Integer Input
On entry: the number of terms of the time series being generated.
Constraint: n0.
3: itype Nag_ExpSmoothType Input
On entry: the smoothing function.
itype=Nag_SingleExponential
Single exponential.
itype=Nag_BrownsExponential
Brown's double exponential.
itype=Nag_LinearHolt
Linear Holt.
itype=Nag_AdditiveHoltWinters
Additive Holt–Winters.
itype=Nag_MultiplicativeHoltWinters
Multiplicative Holt–Winters.
Constraint: itype=Nag_SingleExponential, Nag_BrownsExponential, Nag_LinearHolt, Nag_AdditiveHoltWinters or Nag_MultiplicativeHoltWinters.
4: p Integer Input
On entry: if itype=Nag_AdditiveHoltWinters or Nag_MultiplicativeHoltWinters, the seasonal order, p, otherwise p is not referenced.
Constraint: if itype=Nag_AdditiveHoltWinters or Nag_MultiplicativeHoltWinters, p>1.
5: param[dim] const double Input
Note: the dimension, dim, of the array param must be at least
  • 1, when itype=Nag_SingleExponential or Nag_BrownsExponential;
  • 3, when itype=Nag_LinearHolt;
  • 4, when itype=Nag_AdditiveHoltWinters or Nag_MultiplicativeHoltWinters.
On entry: the smoothing parameters.
If itype=Nag_SingleExponential or Nag_BrownsExponential, param[0]=α and any remaining elements of param are not referenced.
If itype=Nag_LinearHolt, param[0]=α, param[1]=γ, param[2]=ϕ and any remaining elements of param are not referenced.
If itype=Nag_AdditiveHoltWinters or Nag_MultiplicativeHoltWinters, param[0]=α, param[1]=γ, param[2]=β and param[3]=ϕ and any remaining elements of param are not referenced.
Constraints:
  • if itype=Nag_SingleExponential, 0.0α1.0;
  • if itype=Nag_BrownsExponential, 0.0<α1.0;
  • if itype=Nag_LinearHolt, 0.0α1.0 and 0.0γ1.0 and ϕ0.0;
  • if itype=Nag_AdditiveHoltWinters or Nag_MultiplicativeHoltWinters, 0.0α1.0 and 0.0γ1.0 and 0.0β1.0 and ϕ0.0.
6: init[dim] const double Input
Note: the dimension, dim, of the array init must be at least
  • 1, when itype=Nag_SingleExponential;
  • 2, when itype=Nag_BrownsExponential or Nag_LinearHolt;
  • 2+p, when itype=Nag_AdditiveHoltWinters or Nag_MultiplicativeHoltWinters.
On entry: if mode=Nag_InitialValuesSupplied, the initial values for m0, r0 and s-j, for j=0,1,,p-1, used to initialize the smoothing.
If itype=Nag_SingleExponential, init[0]=m0 and any remaining elements of init are not referenced.
If itype=Nag_BrownsExponential or Nag_LinearHolt, init[0]=m0 and init[1]=r0 and any remaining elements of init are not referenced.
If itype=Nag_AdditiveHoltWinters or Nag_MultiplicativeHoltWinters, init[0]=m0, init[1]=r0 and init[2] to init[2+p-1] hold the values for s-j, for j=0,1,,p-1. Any remaining elements of init are not referenced.
7: var double Input
On entry: the variance, σ2 of the Normal distribution used to generate the errors εi. If var0.0 then Normally distributed errors are not used.
8: r[dim] double Communication Array
Note: the dimension, dim, of the array r must be at least
  • 13, when itype=Nag_SingleExponential,Nag_BrownsExponential or Nag_LinearHolt;
  • 13+p, when itype=Nag_AdditiveHoltWinters or Nag_MultiplicativeHoltWinters.
On entry: if mode=Nag_ContinueNoUpdate or Nag_ContinueAndUpdate, r must contain the values as returned by a previous call to g05pmc, r need not be set otherwise.
On exit: if mode=Nag_ContinueNoUpdate, r is unchanged. Otherwise, r contains the information on the current state of smoothing.
Constraint: if mode=Nag_ContinueNoUpdate or Nag_ContinueAndUpdate, r must have been initialized by at least one call to g05pmc or g13amc with modeNag_ContinueNoUpdate, and r must not have been changed since that call.
9: state[dim] Integer Communication Array
Note: the dimension, dim, of this array is dictated by the requirements of associated functions that must have been previously called. This array MUST be the same array passed as argument state in the previous call to nag_rand_init_repeatable (g05kfc) or nag_rand_init_nonrepeatable (g05kgc).
On entry: contains information on the selected base generator and its current state.
On exit: contains updated information on the state of the generator.
10: e[en] const double Input
On entry: if en>0 and var0.0, a vector from which the errors, εt are randomly drawn, with replacement.
If en0, e is not referenced.
11: en Integer Input
On entry: if en>0, the length of the vector e.
If both var0.0 and en0 then εt=0.0, for t=1,2,,n.
12: x[n] double Output
On exit: the generated time series, xt, for t=1,2,,n.
13: fail NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_ENUM_INT
On entry, p=value.
Constraint: if itype=Nag_AdditiveHoltWinters or Nag_MultiplicativeHoltWinters, p2.
NE_INT
On entry, n=value.
Constraint: n0.
NE_INT_ARRAY
On entry, some of the elements of the array r have been corrupted or have not been initialized.
NE_INTERNAL_ERROR
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.
NE_INVALID_STATE
On entry, state vector has been corrupted or not initialized.
NE_NO_LICENCE
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.
NE_REAL_ARRAY
Model unsuitable for multiplicative Holt–Winter, try a different set of parameters.
On entry, param[value]=value.
Constraint: 0param[i]1.
On entry, param[value]=value.
Constraint: if itype=Nag_BrownsExponential, 0<param[i]1.
On entry, param[value]=value.
Constraint: param[i]0.

7 Accuracy

Not applicable.

8 Parallelism and Performance

g05pmc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

None.

10 Example

This example reads 11 observations from a time series relating to the rate of the earth's rotation about its polar axis and fits an exponential smoothing model using g13amc.
g05pmc is then called multiple times to obtain simulated forecast confidence intervals.

10.1 Program Text

Program Text (g05pmce.c)

10.2 Program Data

Program Data (g05pmce.d)

10.3 Program Results

Program Results (g05pmce.r)
GnuplotProduced by GNUPLOT 4.6 patchlevel 3 100 150 200 250 300 350 0 5 10 15 20 25 30 35 Data Time Example Program Exponential Smoothing (95% confidence intervals (CIs) are shown) gnuplot_plot_1 observed gnuplot_plot_2 smoothed gnuplot_plot_3 forecast gnuplot_plot_4 estimated CI gnuplot_plot_5 gnuplot_plot_6 simulated CI gnuplot_plot_7 gnuplot_plot_8 bootstrapped CI gnuplot_plot_9