It depends on the nature of your data. Have you used the stl function
to decompose your time series data?
plot(stl(time series, s.window="periodic"))
Are you looking at the ACF and PCF to see how strong the
autocorrelations are? You may need to use a differencing operator to
make your series stationary? Have you considered a box jenkins
transformation of your data? (taking some kind of log of your data)
Also, what is a very useful measure that I'm not quite sure how to
calculate in R, (I had to copy my fits into excel and compare with my
actuals) is MAPE or Mean Average Proportional Error.
The formula looks like:
SUM for i from 1 to n absolutevalue (1/n *(Actual_i-Fit_i)/Actual_i )
If youre MAPE is below 0.2 your model should be ok for forecasting. In
my experience having it below 0.05 helps make better forecasts. MAPE
isn't a perfect measure (I've found some literature on improved
measures,) ideally you still want the AICC low but it helps figure out
on average how by much your model is off. Sometimes ARIMA models can
have a high MAPE but (relatively )low AICC.
Another model that you could use if there is a trend and seasonality is
Holt Winters method. It's a fairly simple model that works However,
your forecasts have to stay inside the seasonal length measure for the
HW method or the bounds will become rediculously large.
If I knew how state space/GARCH models worked I'd let you know. Another
list to check out is gmae.comp.lang.r.r-metrics
Hope that helps,
-Max
Ozcan Asilkan wrote on 12/04/2007 :> Hello,
>
> In order to do a future forecast based on my past Time Series data sets
> (salespricesproduct1, salespricesproduct2, etc..), I used arima() functions
> with different parameter combinations which give the smallest AIC. I also
> used auto.arima() which finds the parameters with the smallest AICs. But
> unfortuanetly I could not get satisfactory forecast() results, even
> sometimes catastrophic results which made me very disappointed.
>
> Note that, I basically use plot(forecast(auto.arima(invecTS), 24))
statement
> to construct model with arima, forecast 24 future values & plot the
results.
>
> Could you suggest me better forecasting methods that I can apply in R ?
>
> Thanks, best regards..
>
> Ozcan
>
> [[alternative HTML version deleted]]
>
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