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Topic: Time Series Analysis Help
Replies: 10   Last Post: Dec 7, 2007 8:43 PM

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Idgarad

Posts: 20
Registered: 10/18/06
Re: Time Series Analysis Help
Posted: Dec 7, 2007 9:13 AM
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On Dec 6, 2:57 pm, aruzinsky <aruzin...@general-cathexis.com> wrote:
> On Dec 6, 1:46 pm, Idgarad <idga...@gmail.com> wrote:
>
>
>
>
>

> > On Dec 3, 7:01 pm, aruzinsky <aruzin...@general-cathexis.com> wrote:
>
> > > On Dec 3, 3:08 pm, Idgarad <idga...@gmail.com> wrote:
>
> > > > On Dec 3, 1:21 pm, aruzinsky <aruzin...@general-cathexis.com> wrote:
>
> > > > > On Nov 20, 5:24 pm, Idgarad <idga...@gmail.com> wrote:
>
> > > > > > ...
> > > > > > coming back to ARIMA(0,1,1)(0,1,1) with a seasonal period of 12 weeks.
> > > > > > ...

>
> > > > > Thirty years ago, I was well versed in time series analysis but have
> > > > > forgotten > 90%.

>
> > > > > What is the significance of the second "(0,1,1)?"
>
> > > > > According to
>
> > > > >http://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average
>
> > > > > , it doesn't belong there.
>
> > > > The seasonal portion- Hide quoted text -
>
> > > > - Show quoted text -
>
> > > The notation doesn't ring a bell. I suspect you differenced, 1 -
> > > B^12, where B is backshift operator, to remove a periodic component.
> > > I seem to recall that a common mistake (in my day) is to unnecessarily
> > > combine (1 - B) to remove a trend with (1 - B^n) to remove a periodic
> > > compomponent, because any difference, (1 - B^k), also removes a trend
> > > in the original series. Did you make this mistake?- Hide quoted text -

>
> > > - Show quoted text -
>
> > ---(From Duke)----
> > A seasonal ARIMA model is classified as an ARIMA(p,d,q)x(P,D,Q) model,
> > where P=number of seasonal autoregressive (SAR) terms, D=number of
> > seasonal differences, Q=number of seasonal moving average (SMA) terms
> > ---()---

>
> > That's what I was referring to, as I mentioned I am on my own on
> > learning all this, feel free to slap some sense into me as needed.

>
> > I have gone back through though and found a calendar of events that I
> > can factor into the projection, the seasonality is now 52 weeks rather
> > then 12 as there are considerable differences in the activities from
> > quarter to quarter.

>
> > Regardless though I have run into an additional snag (i.e.
> > Requirement) is that any model I use I have to backforecast to show
> > the accuracy of the model against know existing data. Oh brother, I
> > feel like Charlie Brown today.

>
> > What this is all about is there is a mainframe with different virtual
> > computers inside. I have to forecast each virtual computer's usage and
> > factor that against capacity to figure out when all hell is going to
> > break loose.

>
> > In short:
>
> > A : Is production, anything A doesn't use can be borrowed.
> > B-G : are virtual computers. They get to use a given amount but if
> > they need to can borrow A's left overs.

>
> > I need to learn how to do a seasonally sensitive forecast of A-G
> > (separately) so I can determine how much they can borrow (if any).- Hide quoted text -

>
> > - Show quoted text -
>
> The big P and Q doesn't ring a bell.
>
> I understand that you want an estimator that incorporates seasonally
> periodic information, but how far in the future do you want to
> forecast?- Hide quoted text -
>
> - Show quoted text -


Me personally, I am only interested in going 2 quarters ahead (roughly
24 weeks) but, if a reasonable amount of accuracy is possible a year
at most (Which would allow for some nice "What-If" checks). The graph
I produce is a sliding 52 week graph so 1/2 of which is existing known
data and the second half would be the projections (Thus the last known
data point is always in the middle of the graph.)




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