[R] Re: Load prediction

[Johanus Dagius]

Hello,

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
feature splits.
 
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?

Thank you,
Johanus Dagius

At 12:13 PM 6/21/02 -0700, I wrote:
> Hello,
>
>This is perhaps more of a regression question than R,
>but I am learning both, so would appreciate your
>wisdom here.
>
>
>I have some data which reflects power load for an
>electrical generating system, with some temporal
>features. The data fields look like this:
>
>
>ID,MON,DAY,YR,HR,WDAY,DRYBULB,WETBULB,LOAD
>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
and
>temperatures for problems like this?
>
>
>Thank you,
>Johanus Dagius


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