Time series prediction

[miro99_ale]

Hi everybody,
I need your help! :-)
I have a series of data, registered from a sensor regarding the power
consuming of a server in a time series... Something like this:

timestamp   power
5   288,6
16   289,1
26   289,4
80   286,3
134   286,3
190   288,2
244   ...

What I intended to do is to configure a time series predictor with octave in
order to obtain the best ever possibile prediction on the next value (I have
a set of constraints on the data to respect, e.g. data < 300 W, so I need
the most accurate prediction as possible).
The increasing gap between the timestamp is not a problem: I can assume this
is a discrete time series and timestamp could also be called "1, 2, 3, 4,
etc"...
So, how I could solve this mess?
I already read something about time series prediction, here in the general
forum, that suggest the use of TISEAN or something related to the System
Identification Toolbox... Anyway, what I think I really need, before a magic
code which solve my problem, is a way to act:
- I need to identify an ARMA model on my data? How I can do it with octave?
- I need to calculate the W(z)? How I can do it with octave?
- Which is the minimum number of data whit which I could have an optimum
prediction?
- How I can calculate the probability that the prediction match with the
next real value? How I can do it with octave?

Waiting your reply
Best Regards

Alessandro

--
View this message in context: 
http://octave.1599824.n4.nabble.com/Time-series-prediction-tp4247089p4247089.html
Sent from the Octave - General mailing list archive at Nabble.com.