MATLAB code for fitting ARMA/ARIMA models?

[Brian Borchers]

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?

[Michael Robbins]

see www.mathtools.net

Michael Robbins, CFA
Director, Debt Capital Markets
CIBC World Markets Corp., Canadian Imperial Bank of Commerce
New York, NY USA
michael...@us.cibc.com , rob...@bloomberg.net

[Michael Robbins]

>> help stats

Statistics Toolbox.
Version 3.0 (R12) 1-Sep-2000

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[Arthur Jutan]

see System Identification TB
-aj

[Rajiv Singh]

Brian,
Please see "ARMAX", and "PEM" in the System Identification toolbox.

For time series, the function "PRONY" in Signal Processing toolbox, and the
function "HPRONY" (which is based on PRONY) in the HOSA (Higher Order
Spectral Analysis) toolbox can also be used.

hth,
Rajiv Singh.

"Brian Borchers" <borc...@rainbow.nmt.edu> wrote in message
news:9ai63s$1n5a$1...@newshost.nmt.edu...

[Michael Robbins]

Hmmm. sorry. It's not here. It seems something like that should be...

Maybe TMW will make an ARMA toolbox :)

[Vamshi Krishna]

borc...@rainbow.nmt.edu (Brian Borchers) wrote in message
<9ai63s$1n5a$1...@newshost.nmt.edu>...

Hi,

I am also working on ARIMA/ARMA,

Can you have a worked example that you did with arima,

I know a little theory on ARIMA, but I could not find these
worked problems.

Can you provide this at least one numerical example/ Flow
chart

I got your mail id from maths central mats works(forum)

I eagerly wait for response.

Thanks

T.V. Krishna

Analyst(R&D)

[li Li]

borc...@rainbow.nmt.edu (Brian Borchers) wrote in message <9ai63s$1n5a$1...@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.

[Rajiv Singh]

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" <song...@hotmail.com> wrote in message
news:gcvdhe$1nq$1...@fred.mathworks.com...

[Paul]

Google 'armasa delft university'

and see if this code is of any help to you.

[Evan Ruzanski]

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...@msn.com> wrote in message <gh756j$4dk$1...@fred.mathworks.com>...

[soleima...@gmail.com]

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