[Gretl-users] Time Series Analysis with Gretl (How-Tos?)

[Sven Schreiber]

Hi,

from what I understand your plans should be do-able with gretl. But it 
sounds like you need an econometric consultant, some of your questions 
seem pretty broad in terms of scope. And: Before you try to find the 
best model and continually update it, start by fitting one model with 
your existing data. I promise that will keep you busy for a while.

good luck,
sven

Idgarad schrieb:
> I have recently been tasked in developing some forecasts for some CPU usage.
>
> I currently have found data from 2003 to current. The data is weekly
> on a Julian calendar (52 weeks a year).
>
> I have also found a calendar of holidays and planned deadlines and
> activities that may affect the usage (they are dummy variables I
> suppose, with a 1 indicating yes and 0 for no.)
>
> Now after about a month of pouring through every scrap of online lore
> I could find, and with some assistance from several people I have a
> slightly better idea of what to do.
>
> I have to do a time series analysis of the CPU usage in relation to
> seasonal activities (seasonality being 52) along with the the dummy
> variables indicating various activities that affect the cpu usage.
>
> As I am not (big not here) a statistics guru (I'm a a techie not a
> mathematician) and this is more for the sake of research to make my
> life easier. I have $0.00 to dedicate to this (I can't even get a book
> out of this...) so Open Source is my only option.
>
> I would like to use Gretl to develop a 52 week forecast based on past
> historical data.
>
> I have the date (actual and week #) and the known cpu usage in MIPS
> (million of instructions per second).
>
> So far:
> Date,MIPS
>
> Then I have anywhere from 10 to 300 (depending on what I want to add)
> columns of potential events that could affect the MIPS. (dummy
> variable, 0 or 1, holidays, weather, hell even the price of oil if we
> choose).
>
> After all this research ARIMA (Box-Jenkins) seems to be the system to
> use. I have used Autobox to get familiar with this but there is no way
> to back-forecast in that tool that I can see (We want to be able to
> check the model against older data for accuracy and graph if) and I
> would really like to have a better understanding of this process.
> Ideally I would like to have this as automated as possible (that's
> what I do for a living) but not having the strong backgound in
> statistics I am trying to automate something I barely understand.
>
> Each week I get a new data point to add to my master list so we want
> to develop a script\pipeline\process and need to re-run this weekly
> (plus perhaps make some adjustments in future calendar entries based
> on changes). (in short back-forecast 52 week including now and give me
> the next 52 weeks, trim graph to that period...)
>
> But, lost, alone, and frightened in the dark woods of statistics I
> need help building this process using Gretl (and if necessary R).
>
> But where do I begin? How should I structure such a script for Gretl?
>
> Ok I have Gretl... Check!
> I have R also... Check! (just in case)
> I have all the data ready to go in excel files (that's how I get them)... Check!
>
> How do I put this puzzle together? How, in Gretl do I determine the
> best ARIMA model through a script? Do I just loop through?
> Suggestions?
>
> R appears to have some functionality for autofitting, what process
> should I go through via Gretl to find things like this out script
> wise.
>
> I'm lost and I hear wolves and strange noises coming from the forests
> (sounds like albino calculus monsters... I heard the differential ones
> are poisonous...)
>
> I know there has to be others out there that are in the same boat and
> I am hoping to put an easy guide together so others can use it as a
> template. Where do I begin? How do I know when to end? How can I use
> Gretl to accomplish this goal?
>
> Help! The fire is dying and I hear rustling in the bushes nearby! (and
> for the Jetson fans HEPL!)
>
> Id.
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