Hello all,
I am looking for some help in understanding how to change the way R
optimizes the smoothing constant selection process for the HoltWinters
function.
I'm a SAS veteran but very new to R and still learning my way around.
Here is some sample data and the current HoltWinters code I'm using:
rawdata <- c(294, 316, 427, 487, 441, 395, 473, 423, 389, 422, 458, 411,
433, 454, 551, 623, 552, 520, 553, 510, 565, 547, 529, 526, 550, 577, 588,
606, 595, 622, 603, 672, 733, 793, 890, 830)
timeseries_01 <- ts(rawdata, frequency=12, start=c(2009,1))
plot.ts(timeseries_01)
m <- HoltWinters(timeseries_01, alpha = NULL, beta = NULL, gamma = TRUE,
seasonal = c("multiplicative"),
start.periods = 2, l.start = NULL, b.start = NULL, s.start =
NULL)
p <- predict(m, 24, prediction.interval = TRUE)
plot(m, p)
My problem is that I disagree with how R is choosing these smoothing
constants and I would like to explore how some of the other methodologies
listed in the OPTIM function [such as Nelder-Mead, BFGS, CG, L-BFGS-B,
SANN, and Brent], but it is unclear to me how I would go about doing this.
For example, the above code results in the following constants:
alpha: 0.7952587
beta : 0.01382988
gamma: 1
However, using alternate software, I find that...
alpha: 0.990
beta : 0.001
gamma: 0.001
...actually fit this series much better, thus I would like to see if I can
adjust R to reproduce this method of optimizing the three smoothing
constants.
Can anyone help?
Thank you,
Jonathan
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