Hi Michael,
I think you're right, I should be looking for predict instead of
forecast. I'm still fairly new to R so often don't know what to look
for. As a simplified example (let's neglect the fourier terms):
fit = auto.arima(data)
but now I have data.latest, so I want to use the ARIMA terms from fit
but with data.latest
I'll look into predicting, thank you
On 1/20/2012 1:35 AM, Michael Weylandt [via R] wrote:
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 [hidden email]
/user/SendEmail.jtp?type=nodenode=4312525i=0 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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