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From: Evan Ruzanski on 23 Dec 2009 20:17
This is correct.

Also note that you can specify a differencing order using DIFF(X,ORDER) based on the trend (stationarity) observed in your data.

And remember too if you do forecasting this way that you have to integrate the resulting predicted time series by recursively summing the predicted values using the last observation value as your initial condition.

"Rajiv Singh" <rajiv_singh(a)msn.com> wrote in message <gh756j$4dk$1(a)fred.mathworks.com>...
> ARIMA model can be created by using differenced data with ARMAX. For
> example, use diff(x) rather than x as output data and then using ARMAX
> command. A pole at 1 can be added to resulting model to achieve a true ARIMA
> model.
>
> m = armax(diff(x),[p,q])
> m.a = conv(m.a,[1 -1]);
>
> Rajiv
>
> "li Li" <songzi91(a)hotmail.com> wrote in message
> news:gcvdhe$1nq$1(a)fred.mathworks.com...
> > borchers(a)rainbow.nmt.edu (Brian Borchers) wrote in message
> > <9ai63s$1n5a$1(a)newshost.nmt.edu>...
> >> Does anyone out there have a MATLAB code for fitting ARMA models (with
> >> specified autoregressive order p and moving average order q) to time
> >> series data?
> >> I also have a problem like this. Although I have the ARIMA model as
> >> following, I do not know how to fit it.
> >
> > u=iddata(timeseries)
> > m = armax(u,[p q]) %ARMA(p,q)
> >
> > result:
> > Discrete-time IDPOLY model: A(q)y(t) = C(q)e(t)
> > A(q) = 1 - 1.216 q^-1 + 0.7781 q^-2
> >
> > C(q) = 1 - 1.362 q^-1 + 0.8845 q^-2 + 0.09506 q^-3
> >
> > Estimated using ARMAX from data set z
> > Loss function 0.000768896 and FPE 0.00084009
> > Sampling interval: 1
> >
> > Is there anyone have the idea how to use matlab code to fitting it?
> >
> > Thanks.
> >
> >
> >
>
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