[R] Re: Load prediction
jdagius at yahoo.com
Sun Jun 23 04:35:41 CEST 2002
I have received no reply to my previous query, so I
will try again.
I have tried glm on this problem with the default
parameters and it produced a model with mean absolute
error of approx 300 MWhrs. (The data is roughly
normally distributed with a mean of 1700 MWhrs and
SD=500). I know very little about R and so I am not
sure what parameter needs to be tweaked from here.
Using Cubist (www.rulequest.com) I have created a
predictive model whose mean error is around 100 MWhrs.
Cubist builds a recursively partitioned tree using
piecewise linear regression. Cubist also outputs a
nice set of rules which explain the model in terms of
I think R should give a comparable result. Does R have
a method of piecewise approximation like this? I would
like to compare R against Cubist. What method(s)in R
must I learn to do this?
At 12:13 PM 6/21/02 -0700, I wrote:
>This is perhaps more of a regression question than R,
>but I am learning both, so would appreciate your
>I have some data which reflects power load for an
>electrical generating system, with some temporal
>features. The data fields look like this:
>4455 5 13 92 13 4 70 63 1617
>4456 3 9 92 13 2 73 57 1397
>4457 10 5 92 8 2 58 58 1501
>4458 11 24 92 18 3 56 56 1885
>4459 9 27 92 8 1 65 65 1402
>What R methodology is likely to produce the most
>accurate load forecast prediction for a given date
>temperatures for problems like this?
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