Can you clarify what exactly you mean by this?
"[N]ow [I] would like to use the last X values to predict tomorrow's
weather. I'm at a loss. All the functions I've come across (like
forecast()) use the series and then forecast from the end point."
It sounds like a prediction to me.
Anyways, I think most methods do allow "new" values for the
independent variables: e.g., the newdata argument to most predict()
methods and the xreg arguments to forecast::forecast(). Do you know
which method you are using?
Hope this helps,
Michael
On Wed, Jan 18, 2012 at 4:17 PM, nhomeier <nhomeier at aer.com>
wrote:> Couldn't find this in the archives. I'm fitting a series of
historical
> weather-related data, but would like to use the latest values to forecast.
> So let's say that I'm using 1970-2000 to fit a model (using fourier
terms
> and arima/auto.arima), but now would like to use the last X values to
> predict tomorrow's weather. I'm at a loss. All the functions
I've come
> across (like forecast()) use the series and then forecast from the end
> point.
>
> Do I need to decompose the fit and write it out the long way? For example,
> Tomorrow = fit$coef[1]*Yesterday + fit$coef[2]*BeforeYesterday + etc
>
> or is there a function that I'm not finding?
>
> Thank you,
> Nicole
>
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